diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..b20c92d
Binary files /dev/null and b/.DS_Store differ
diff --git a/data/.DS_Store b/data/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/data/.DS_Store differ
diff --git a/data/.ipynb_checkpoints/main-checkpoint.ipynb b/data/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..eb4540c
--- /dev/null
+++ b/data/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -0,0 +1,1661 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import pearsonr, spearmanr\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 1\n",
+    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def roll_dice():\n",
+    "    rolls = []\n",
+    "    my_dict = {}\n",
+    "    index = 1\n",
+    "    for i in range(10):\n",
+    "        roll = random.randint(1, 6)\n",
+    "        rolls.append(roll)  \n",
+    "        my_dict[index] = {\"Roll\" : i+1, \"Value\" : roll}\n",
+    "        index += 1\n",
+    "    df = pd.DataFrame.from_dict(my_dict, orient=\"index\")\n",
+    "  \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Roll  Value\n",
+       "1      1      2\n",
+       "2      2      4\n",
+       "3      3      6\n",
+       "4      4      2\n",
+       "5      5      3\n",
+       "6      6      3\n",
+       "7      7      5\n",
+       "8      8      4\n",
+       "9      9      2\n",
+       "10    10      5"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Roll  Value\n",
+       "1      1      2\n",
+       "2      2      3\n",
+       "3      3      6\n",
+       "4      4      2\n",
+       "5      5      2\n",
+       "6      6      4\n",
+       "7      7      2\n",
+       "8      8      4\n",
+       "9      9      4\n",
+       "10    10      1"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = roll_dice()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=\"Value\")\n",
+    "plt.plot(df[\"Value\"].values)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_df = df.groupby(\"Value\").agg(\"count\").reset_index()\n",
+    "plt.plot(f_df[\"Value\"],f_df[\"Roll\"])\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe difference between thoose two plot is the following:\\n\\nthe first plot shows the values of the rolls, sorted by value\\nthe second plot shows the count of each value, unsorted \\n\\nthe relation is only the count \\n'"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The difference between thoose two plot is the following:\n",
+    "\n",
+    "the first plot shows the values of the rolls, sorted by value\n",
+    "the second plot shows the count of each value, unsorted \n",
+    "\n",
+    "the relation is only the count \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 2\n",
+    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+    "\n",
+    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.2"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def summary_stats(df, column):\n",
+    "    x = 0\n",
+    "    for i in df[column]:\n",
+    "        x += i\n",
+    "    mean = x/len(df)\n",
+    "    return mean\n",
+    "\n",
+    "summary_stats(roll_dice(), \"Value\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Roll</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Value  Roll\n",
+       "0      1     1\n",
+       "1      2     4\n",
+       "2      3     1\n",
+       "3      4     3\n",
+       "4      6     1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df = df.groupby(\"Value\").agg(\"count\").reset_index()\n",
+    "display(sorted_df)\n",
+    "mean = sum(sorted_df[\"Roll\"]) / len(sorted_df[\"Roll\"])\n",
+    "print(round(mean,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_median():\n",
+    "    x = df[\"Value\"].reset_index(drop=True)\n",
+    "    n = len(\"Value\")\n",
+    "    if n%2 == 0:\n",
+    "        return (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        return x.iloc[int((n-1)/2)]\n",
+    "    \n",
+    "median = get_median()\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 2\n",
+      "Q2: 2.5\n",
+      "Q3: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "def quartiles():\n",
+    "    x = df[\"Value\"].reset_index(drop=True)\n",
+    "    x = x.sort_values()\n",
+    "    n = len(x)    \n",
+    "\n",
+    "    if n % 4 == 0:\n",
+    "        q1 = (x.iloc[int(n / 4)] + x.iloc[int(n / 4) - 1]) / 2\n",
+    "        q3 = (x.iloc[int(n * 3 / 4)] + x.iloc[int(n * 3 / 4) - 1]) / 2\n",
+    "    else:\n",
+    "        q1 = x.iloc[int(n / 4)]\n",
+    "        q3 = x.iloc[int(n * 3 / 4)]\n",
+    "        \n",
+    "\n",
+    "    if n % 2 == 0:\n",
+    "        q2 = (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        q2 = x.iloc[int((n - 1) / 2)]\n",
+    "\n",
+    "    return q1, q2, q3\n",
+    "\n",
+    "    \n",
+    "result_q1, result_q2, result_q3 = quartiles()\n",
+    "print(\"Q1:\", result_q1)\n",
+    "print(\"Q2:\", result_q2)\n",
+    "print(\"Q3:\", result_q3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n",
+      "2.5\n",
+      "4.0\n",
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Value\"].quantile(0.25))\n",
+    "print(df[\"Value\"].quantile(0.5))\n",
+    "print(df[\"Value\"].quantile(0.75))\n",
+    "print(df[\"Value\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 3\n",
+    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+    "#### 1.- Sort the values and plot them. What do you see?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "1            1     1      2\n",
+       "2            2     2      6\n",
+       "3            3     3      1\n",
+       "4            4     4      6\n",
+       "..         ...   ...    ...\n",
+       "95          95    95      4\n",
+       "96          96    96      6\n",
+       "97          97    97      1\n",
+       "98          98    98      3\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "\n",
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_df.drop(\"Unnamed: 0\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    roll  value\n",
+       "0      0      1\n",
+       "1      1      2\n",
+       "2      2      6\n",
+       "3      3      1\n",
+       "4      4      6\n",
+       "..   ...    ...\n",
+       "95    95      4\n",
+       "96    96      6\n",
+       "97    97      1\n",
+       "98    98      3\n",
+       "99    99      6\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvG0lEQVR4nO3df3CV1Z3H8c/l15VgSAXKvYlESFl2EaPCgkuDtLAqrI5LcZxpLajFttsBodTIdqGsu1vWtUlDtyy7zSwd4ixFGaR/dKh0tirMqHEZwhRDaSU6WjEii2SyxmxuQAxr8uwf4V4JSUhu7vOce+553q+ZzJSbR3u4E5Nvvp/vOSfieZ4nAAAAiwzL9gIAAAAuR4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsMyLbCxiKrq4uvf/++8rPz1ckEsn2cgAAwCB4nqf29nYVFRVp2LAr90hyskB5//33VVxcnO1lAACAITh16pQmTZp0xWdyskDJz8+X1P0XHDt2bJZXAwAABiORSKi4uDj1c/xKcrJAScY6Y8eOpUABACDHDGY8gyFZAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgnbQLlFdeeUVLlixRUVGRIpGIfvnLX/b4vOd52rRpk4qKijR69GgtXLhQDQ0NPZ7p6OjQ2rVrNWHCBI0ZM0Zf+tKX9N///d8Z/UUAAIA70i5Qzp07p5tvvlnV1dV9fn7z5s3asmWLqqurdeTIEcXjcS1atEjt7e2pZ8rLy7V3717t2bNHBw8e1NmzZ/WXf/mX6uzsHPrfBAAAOCPieZ435H84EtHevXt1zz33SOrunhQVFam8vFwbNmyQ1N0ticViqqqq0sqVK9XW1qbPfvazevrpp3XfffdJ+vTo+l//+tf6i7/4iwH/fxOJhAoKCtTW1sZBbQAA+Kizy9NvGj9Uc/vHmph/lf6sZJyGD/Pn3rt0fn77epJsY2OjmpqatHjx4tRr0WhUCxYs0KFDh7Ry5UrV19fr//7v/3o8U1RUpNLSUh06dKjPAqWjo0MdHR2pPycSCT+XDQAAJD1//Iz+8Vev60zbx6nXCguu0veXzNCdpYVG1+LrkGxTU5MkKRaL9Xg9FoulPtfU1KRRo0bpmmuu6feZy1VWVqqgoCD1wUWBAAD46/njZ/TwrqM9ihNJamr7WA/vOqrnj58xup5AdvFcfsa+53kDnrt/pWc2btyotra21MepU6d8WysAAGHX2eXpH3/1uvqa+Ui+9o+/el2dXUOeCkmbrwVKPB6XpF6dkObm5lRXJR6P68KFC2ptbe33mctFo9HUxYBcEAgMTmeXp7oTLXr22GnVnWgx+o0FQG75TeOHvTonl/IknWn7WL9p/NDYmnwtUEpKShSPx3XgwIHUaxcuXFBtba3mzZsnSZo9e7ZGjhzZ45kzZ87o+PHjqWcAZOb542c0v+pFLas5rEf2HNOymsOaX/Wi8RYtgNzQ3N5/cTKU5/yQ9pDs2bNn9fbbb6f+3NjYqGPHjmncuHG67rrrVF5eroqKCk2bNk3Tpk1TRUWF8vLytHz5cklSQUGBvvnNb+qv//qvNX78eI0bN07f/e53deONN+qOO+7w728GhFQyR768X5LMkbc98KfGh90A2G1i/lW+PueHtAuUV199VX/+53+e+vO6deskSStWrNDPfvYzrV+/XufPn9fq1avV2tqquXPnav/+/crPz0/9M//yL/+iESNG6Ctf+YrOnz+v22+/XT/72c80fPhwH/5KQxfk1irAhIFy5Ii6c+RFM+J8bQNI+bOScSosuEpNbR/3+f0jIile0P1z0ZSMzkHJliDOQbFpaxUwVHUnWrSs5vCAzz3zrc+rbOp4AysCkCuS3VdJPYqU5K8yfnRf0/n5zV08sm9rFTBUNubIAHLDnaWF2vbAnype0DPGiRdclZVo2NeD2nIRLXG4xMYcGUDuuLO0UItmxK0Ydwh9B8XGrVXAUCVz5P6+lUTUHV2azJEBYChC30GhJQ6XDB8W0feXzNDDu44qor5z5O8vmUE3EECfbJrHDH0HhZY4XGNbjgwgN9g2jxn6DoqNW6uATNmUI7uO4wngAhvnMUNfoNASh6uGD4uwlThgNrXDgUykM49p6vtK6CMeiZY4gPTZ1g4HMmHjPGboOyhJd5YW6rbpMT1d965OfviRJo/L04NlUzRqBDUcgJ5sbIcDmbBxHpMC5aK+WrVPHmykVQugFxvb4UAmbJzHpD0gWrUA0mNjOxzIRHIeU1Kvc5SyNY8Z+gJloFat1N2q7ezKuSuLAATExnY4kCnb5jFDH/HQqgWQLhvb4YAfbDqiIPQFCq1aAOlKtsNXXbz59XKeOJ4AucuWIwpCH/HQqgUAwD6hL1C4XA1AupKza/1JbjNmdg25qLPLU92JFj177LTqTrRk7es49BEPJ8kCSBeza3CVTacjh76DItk3uQzAbsyuwUW2HbkR+g5Kkk2TywDsxuwaXGPj6cgUKJewZXIZ8AO37AaHbcZwjY2xJQUK4CCbcmQXMbsG19gYWzKDAjjGthzZVcyuwSU2xpZ0UACH2Jgju4zZNbjCxtiSDgrgkHRyZPgjObu2dOa1Kps6nuIEOSkZW/Z34kk2TkemQAEcYmOODABDQYFyCVtOzwOGysYcGYD9bDwdmRmUi9j1ABfYmCMDsJ+N24zpoIhdD3BHMkeW1Ot+Kba/AuiPjfFw6AuUgXY9SFz6hdzC9lcA6bIxHg59xGNjWwvIFNtf4SJORw6OjfFw6AsUG9tagB+4ugEuYU4wWDaejhz6iMfGthYA4FPMCZphWzwc+g6KjW0tAEA3Tkc2687SQt02Paan697VyQ8/0uRxeXqwbIpGjTDfzwh9gWJjWwsA0I05QbP6itKePNiYlSgt9BGPZF9bCwDQjTlBc2yL0kLfQUli1wMA2Ic5QTNsjNIoUC7BrgcAsAtzgmbYGKUR8QAArGXjLbsusjFKo0ABACDkbIzSKFAAR3E7N1xg4y27LkpGaf31oSLqPhiPk2QBZIRTN+EKG2cjXGTjkRt0UADH2LZVEMiEjbMRrrLtyA06KIBDbNwqCGTCxtkIl9l05AYFCuAQ2uFwDduMzbPlyA0iHsAhtMPhmuRshKReA5xcR+I2ChTAIROujvr6HGAD22YjYAYRD+CSwe60ZEcmcoxNsxEwgwIFcMgH5zp8fQ6wiS2zETCDiAdwCDseALiCDsolOrs82ofIabMnX6NhEelKh2oOi3Q/BwA2o0C5iJM34YL6k61XLE6k7uKl/mQrrXIAViPiESdvwh1sMwbgitAXKAOdvClxERVyBzMoAFwR+gIlnZM3AdvZeCOp67g1GghG6GdQaInDJTbeSOoyZteA4IS+g0JLHK7h1E0zmF0DghX6DgoXUcFFnLoZLG6NhstsOXLD9w7KJ598or/7u79TSUmJRo8erc997nN6/PHH1dXVlXrG8zxt2rRJRUVFGj16tBYuXKiGhga/lzIoXEQFVyVP3Vw681qVTR3P17CPmF2Dq54/fkbzq17UsprDemTPMS2rOaz5VS9mpSPoe4FSVVWln/70p6qurtYbb7yhzZs360c/+pF+8pOfpJ7ZvHmztmzZourqah05ckTxeFyLFi1Se3u738sZFFriANLB7BpcZFts6XvEU1dXp6VLl+ruu++WJE2ZMkXPPPOMXn31VUnd3ZOtW7fqscce07333itJ2rlzp2KxmHbv3q2VK1f6vaRBoSUOYLCYXcsOW6IHF9kYW/peoMyfP18//elP9dZbb+mP//iP9bvf/U4HDx7U1q1bJUmNjY1qamrS4sWLU/9MNBrVggULdOjQoT4LlI6ODnV0fHq5WSKR8HvZkriICsDgMLtmHjumgpVObGnq56TvEc+GDRu0bNkyTZ8+XSNHjtSsWbNUXl6uZcuWSZKampokSbFYrMc/F4vFUp+7XGVlpQoKClIfxcXFfi8bAAaN2TWzbIseXGRjbOl7gfLzn/9cu3bt0u7du3X06FHt3LlT//zP/6ydO3f2eC4S6fkfrud5vV5L2rhxo9ra2lIfp06d8nvZAJAWZtfM4LRvM2yMLX2PeP7mb/5G3/ve9/TVr35VknTjjTfq5MmTqqys1IoVKxSPxyV1d1IKCz/9D7i5ublXVyUpGo0qGo36vVQAyAiza8GzMXpwkY2xpe8dlI8++kjDhvX81w4fPjy1zbikpETxeFwHDhxIff7ChQuqra3VvHnz/F4OAASK7dzBsjF6cFEytuyvD+XJfGzpewdlyZIl+sEPfqDrrrtON9xwg377299qy5Yt+sY3viGpO9opLy9XRUWFpk2bpmnTpqmiokJ5eXlavny538sBAOQwG6MHmOF7gfKTn/xEf//3f6/Vq1erublZRUVFWrlypf7hH/4h9cz69et1/vx5rV69Wq2trZo7d67279+v/Px8v5cDAIFi62uwbIweXJSc9elPNrYZRzzPy7nJokQioYKCArW1tWns2LHZXg6AkGLrqxnJXTxS3xdgMpScuboTLVpWc3jA55751uczmvVJ5+d36C8LBIChYOurOeyYCp6Nsz6hvywQANJl46mbrmPHVLBsnPWhQIFxZPbIdWx9zQ5O+w6OjbM+FCgwisweLrCxHQ5kIrnN+OFdRxVR37M+prcZM4MCY8js4Qob2+FApmyb9aGDAiPI7OESG9vhgB/uLC3UbdNjerruXZ388CNNHpenB8umaNQI8/0MChQYQWYPl9jYDgf80FcM/+TBxqzE8EQ8MILMHq6xrR0OZMq2GJ4OCowgs4eL2PpqFjsAg2NjDE+BAiPI7OEqtr6awQ7AYNkYwxPxwIhkZi99mtEnkdkDuBLbogcX2RjDU6DAGDJ7AOkaKHqQuqOHzq6cu1bOKjbG8EQ8lyDfDB6ZPYB02Bg9uMjGGJ4C5SLyTXPI7AEMlo3Rg4ts3DpPxCPyTQCwlY3Rg6tsi+FD30GxcWsVAKCbjdGDy2yK4UPfQUkn3wQAmJWMHvobgfXEDkC/JWP4pTOvVdnU8Vl7b0NfoJBvAgBgn9AXKOSbAGCvZAzfn2QMzzZj94S+QEnmm/01sCLq3s1Dvumfzi5PdSda9Oyx06o70cI3FgD9IoYPr9APydq4tcplbOcGkA5i+PAKfQdFsm9rlavYzg0gXcTw4RX6DkqSTVurXMR2bgBDwTbj8KKDcglbtla5iBwZwFBw0Wh4UaDACHJkAENFDB9ORDwwYsLVUV+fA2zBJaNmEMOHDwUKzBjsTmJ2HCOHsCvNLC4aDRciHhjxwbkOX58Dso1daUCwKFBgBFsF4ZKBdqVJnG4KZIoCBUZwYi9cwq40IHgUKDCCrYJwCbvSgOBRoMAYtgrCFUSWQPDYxQOj2CoIF8yefI2GRaQrjZgMi3Q/B2BoKFBgHFsFkevqT7ZesTiRuouX+pOtfK0DQ0TEAwBpYgYFCB4dFMBRnHAanAljBnky8iCfA9AbBQrgIE44Ddhg6zzqQWDIiHgAx3DCafA+ODvIk5EH+RyA3ihQAIdwwqkZbDMGgkeBAjiEE07N4GRkuKyzy1PdiRY9e+y06k60ZO0XGmZQAIewu8SM5MnID+86qoh6XsLNycjIZTbNr9FBARxC9GAOJyPDNbbNr9FBARySjB6a2j7ucw4lou4foEQP/uBkZLhioPm1iLrn1xbNiBv7+qaDAjgkGT30lxh7InrwW/Jk5KUzr1XZ1PG8t8hJNs6vUaAAABByNs6vUaAADkm2afuTbNOyzRjApWycX6NAuYQtW6uAobKxTQvAfjZunWdI9iKbtlYBQ2VjmxaA/WzcOk8HRfZtrQKGysY2LYDcYNvW+dB3UGzcWgUMFduMzePWaLjEpq3zoS9Q0snsy6aON7cwYAhsbNO6jGgYLkpunc+20Ec8ZPZwjW1tWlcRDQPBCn0HhcweLrqztFC3TY/p6bp3dfLDjzR5XJ4eLJuiUSNC/zuJL4iGgeCFvkAhs4eL+ooenjzYSPTgE6JhIHih/3UqmdlL6rX/m8weuYjoIXhEw0DwQl+gSGT2cMdA0YPESbJ+IBoGghf6iCfJpq1VwFARPZhBNAwEL5AOyunTp/XAAw9o/PjxysvL08yZM1VfX5/6vOd52rRpk4qKijR69GgtXLhQDQ0NQSwlLdxKilxH9GAGt0YDwfO9QGltbdWtt96qkSNH6rnnntPrr7+uH//4x/rMZz6Tembz5s3asmWLqqurdeTIEcXjcS1atEjt7e1+LwcIFaIHAK7wPeKpqqpScXGxduzYkXptypQpqf/teZ62bt2qxx57TPfee68kaefOnYrFYtq9e7dWrlzp95KA0CB6MGOwt0azzRgYOt87KPv27dOcOXP05S9/WRMnTtSsWbNUU1OT+nxjY6Oampq0ePHi1GvRaFQLFizQoUOH+vx3dnR0KJFI9PgA0Bu70szg1mggeL4XKO+88462bdumadOm6YUXXtCqVav0ne98R0899ZQkqampSZIUi8V6/HOxWCz1uctVVlaqoKAg9VFcXOz3sgFnsCsteMz6AMHzPeLp6urSnDlzVFFRIUmaNWuWGhoatG3bNn3ta19LPReJ9PwNzvO8Xq8lbdy4UevWrUv9OZFIUKQAV8CutGBNGBP19TkAvfleoBQWFmrGjBk9Xrv++uv1i1/8QpIUj8cldXdSCgs//U2uubm5V1clKRqNKhrlP3RXcPurGbZc+OWkwX658mUNDJnvBcqtt96qN998s8drb731liZPnixJKikpUTwe14EDBzRr1ixJ0oULF1RbW6uqqiq/lwPLcPsrXPDB2Q5fnwPQm+8zKI8++qgOHz6siooKvf3229q9e7e2b9+uNWvWSOqOdsrLy1VRUaG9e/fq+PHjeuihh5SXl6fly5f7vRxYhCPY4Qq2cwPB872Dcsstt2jv3r3auHGjHn/8cZWUlGjr1q26//77U8+sX79e58+f1+rVq9Xa2qq5c+dq//79ys/P93s5sAS3v5pHlBYctnMDwYt4npdzl3IkEgkVFBSora1NY8eOzfZyMAh1J1q0rObwgM89863PMzfhA6K04CU7gpJ6FCnJEpAdU0Bv6fz85rJAGMG2THOI0sxgOzcQLC4LvAQt8eCQ2ZtBlGYW27mB4FCgXERLPFhk9mZwm7F5bOcGgkHEI1riJnAEuxlEaQBcEfoCZaCWuNTdEu/syrlZYuuQ2QePKA2AK0If8dASN+vO0kLdNj2mp+ve1ckPP9LkcXl6sGyKRo0Ifa3sC6I0AK4IfYFCS9ysvmZ9njzYyKyPT5JR2sO7jiqivre/EqUByAWh/7WVlrg5zPqYQZQGwAWh76DQEjeD7a9msf0VwFDZcuRG6AsUWuJmMOtjHttfAaTLpiM3Qh/xSLTETWDWBwDsZlsMH/oOShIt8WAx6wMA9rIxhqdAuQQt8eAw6wNX2ZLXA5mwMYanQIERzPrARTbl9UAmbIzhmUGBMcz6wCW25fVAJmyM4emgwChOkoULbMzrgUzYGMNToMAoTpKFC2zM64FM2BjD82srjKElDlfYmNcDmbIthqeDAiNoicMlNub1gB9sOnKDAgVG0BKHS2zM6wG/2HLkBhEPjKAlDpck83rp03w+iW3zgD8oUGDEhDFRX58Dss22vB5wDREPzBjsL5L8wukbTjgNnk15PeAaChQY8cHZDl+fw5Vxwqk5tuT1gGuIeGAEux7MYTs3ABdQoMCI5K6H/hrfEXX/hs+uh8wMtJ1b6t7O3dnV1xMAYA8KFBiR3PXQ349FT+x68EM627kBwGYUKIBD2M4NwBUUKDAiGT30J3mSLNFDZpj1AeAKChQYQfRgBrM+5nV2eao70aJnj51W3YkWimzAJ2wzhhFED2bYeCOpy9jODQSHDgqMIHowhxNOzWA7NxAsOiiX4OTN4HC5mlmccBosbucGgkeBchGt2mARPZjHCafB4XZuIHhEPKJVawrRA1zBTBUQvNB3UGjVmnVnaaFumx7T03Xv6uSHH2nyuDw9WDZFo0ZQKyN3MFMFBC/0BQqtWrP6itKePNhIlIacwkwVELzQ/9pKq9YcojS4IjlTJanXmTPMVAH+CH2BQqvWDC6xg2uYqQKCFfqIh1atGURpcBHbueEiW47cCH2BwvZXM4jS4Cq2c8MlNh25EfqIR6JVawJRGgDYzbY5wdB3UJJo1QaLKA2usqUdDmTCxiM3KFAuQas2OERpcJFN7XAgEzbOCRLxwBiiNLjEtnY4kAkb5wTpoMAoojS4wMZ2OJAJG+cEKVBgHFGaGcxGBMfGdjiQCRvnBClQAAcxGxEsG9vhQCZsnBNkBgVwDLMRwbOxHQ5kyrY5QToogEOYjTDDxnZ4GBBbBs+mOUEKFMAhzEaYYWM73HXElubYMidIxAM4hNkIc2xrh7uM2DKc6KDAONq0wWE2wiyb2uGuIrYMLwoUGEWbNljMRphnSzvcVcSW4UXEA2No0wYvORshfToLkcRsBHIRsWV4UaDAiIHatFJ3m7azq68nkA5mI+ASYsvwIuKBEbRpzWI2Aq4gtjTPljnBwDsolZWVikQiKi8vT73meZ42bdqkoqIijR49WgsXLlRDQ0PQS0EW0aY1LzkbsXTmtSqbOp7iBDmJ2NKs54+f0fyqF7Ws5rAe2XNMy2oOa37Vi1mJ4AMtUI4cOaLt27frpptu6vH65s2btWXLFlVXV+vIkSOKx+NatGiR2tvbg1wOsog2LYChIrY0w7Y5wcAinrNnz+r+++9XTU2NnnjiidTrnudp69ateuyxx3TvvfdKknbu3KlYLKbdu3dr5cqVQS0JWUSbFkAmiC2DZeN27sA6KGvWrNHdd9+tO+64o8frjY2Nampq0uLFi1OvRaNRLViwQIcOHerz39XR0aFEItHjA7mFNi2ATBFbBiedOUFTAilQ9uzZo6NHj6qysrLX55qamiRJsVisx+uxWCz1uctVVlaqoKAg9VFcXOz/ohE42rQAYCcb5wR9j3hOnTqlRx55RPv379dVV/U/TxCJ9Kx8Pc/r9VrSxo0btW7dutSfE4kERUqOok0LAPaZcHXU1+f84HuBUl9fr+bmZs2ePTv1Wmdnp1555RVVV1frzTfflNTdSSks/PQ35ubm5l5dlaRoNKpo1NybgmBx8qYZtmwVBJADBnsElcGjqnwvUG6//Xa99tprPV77+te/runTp2vDhg363Oc+p3g8rgMHDmjWrFmSpAsXLqi2tlZVVVV+LwcIJa4UAJCOD851+PqcH3wvUPLz81VaWtrjtTFjxmj8+PGp18vLy1VRUaFp06Zp2rRpqqioUF5enpYvX+73coDQSW4VvPwXneRWQeZ9AFzOxqMgsnKS7Pr163X+/HmtXr1ara2tmjt3rvbv36/8/PxsLAeGET0Ex8atggDsZ+NREBHP83Lu8pNEIqGCggK1tbVp7Nix2V4O0kD0EKy6Ey1aVnN4wOee+dbnmQMC0EOy+yr1HDVJ/irjR/c1nZ/fXBYIY2w7pdBFNm4VBJAbbDsKgssCYQTRgxk25sgAcsedpYW6bXpMT9e9q5MffqTJ4/L0YNkUjRphvp9BgQIjuM3YDBtzZAC5o68Y/smDjVmJ4Yl4YATRgxlcKQBgqGyL4SlQYATRgzm25cgA7DdQDC91x/CdXeb21RDxwAiiB7O4UgBAOmyM4SlQYEQyenh411FF1PcWNqIHf3GlAIDBsjGGJ+KBMUQPAGAnG2N4OigwiujBHE7sBTBYsydfo2ER6UojJsMi3c+ZQoEC44gegseJvQDSUX+y9YrFidRdvNSfbDX2/ZuIB3CMbVsFAdiPGRRA3dFD3YkWPXvstOpOtBjdtuY6G7cKArAfMygIPaKHYNm4VRCA/Ww8CoIOCowhegiejW1aAPaz8RRqChQYQfRgho1tWgC5wbajIIh4YATRgxk2tmkBv7B1Png2HQVBgQIjiB7M4MReuIr5NXNsOQqCiAdGED2YY1ubFsgU82vhRAcFRhA9mGVTmxbIxEDzaxF1z68tmhHn69sxdFBghI0T4q5LtmmXzrxWZVPH894iJ6Uzvwa3UKDAGKIHAOlifi28iHhgFNEDgHRMuDrq63PIHRQoMM6WCXEAOWCwRyNxhJJvbNnOTYECALDWB+c6fH0OV2bTdm5mUAAA1uKIAnNs285NgQLjuM0YwGAljyjoL2CIqPs3fI4oyIyN15EQ8cAom9qHAOzH6chm2HgdCR0UGGNb+xBAbuCIguDZuJ2bDgqM4DRIuMqWHQ+u44iCYNk460OBAiNsbB8CmSKyNIsjCoJj43UkRDwwwsb2IZAJIku4xMbrSChQYISN7UNgqGzc8QBkyrZZHyIeGGFj+xAYKiJLuMqmWR8KFBjBVkG4hMgSLrNl1oeIB8bY1j4EhorIEggeHZRLsF0weDa1D4Ghmj35Gg2LSFcaMRkW6X4OwNBQoFzEdkFzbGkfAkNVf7L1isWJ1F281J9s5WsdGCIiHrFdEEB6mEEBghf6AoXtggDSxQwKELzQFyjpbBcEAIkbdgETQl+g0KoFkK7ktvn++qqe2DYPZCr0BQqtWgAA7BP6AoVWLYB0JWfX+pO8nZvZNeSizi5PdSda9Oyx06o70ZK1r+PQbzPmhFMA6eKoe7jKpiM3Qt9BkTjhFEB6mF2Di2w7ciP0HZQkTjgFMFjMrsE1Ax25kYwtF82IG/u5SIFyCU44BTAY3M4N19gYWxLxAECakrNrknoN2DO7hlxkY2xJgQIAQ8DsGlwy4eqor8/5gYgHcBS3cweP2TU4Y7A7iQ3uOKZAARxk01ZB1zG7Bhd8cK7D1+f8QMQDOMa2rYIA7GfjzjQKFMAh3M4NYChsPFWdAgVwCLdzAxgKGy/ApEABHGLjVkEAGAoKFMAhNubIAOxn4wWYvhcolZWVuuWWW5Sfn6+JEyfqnnvu0ZtvvtnjGc/ztGnTJhUVFWn06NFauHChGhoa/F4KEDo25sius+XmVyATNsbDvhcotbW1WrNmjQ4fPqwDBw7ok08+0eLFi3Xu3LnUM5s3b9aWLVtUXV2tI0eOKB6Pa9GiRWpvb/d7OUCocMKpWc8fP6P5VS9qWc1hPbLnmJbVHNb8qhfZKYWcY2M8HPE8L9By/3/+5380ceJE1dbW6otf/KI8z1NRUZHKy8u1YcMGSVJHR4disZiqqqq0cuXKAf+diURCBQUFamtr09ixY4NcPpCTOAcleMnt3Jd/A02Wfpwmi1xSd6JFy2oOD/jcM9/6fEbn/qTz8zvwg9ra2tokSePGdbeUGxsb1dTUpMWLF6eeiUajWrBggQ4dOjSoAgXAlXHCabBsvPkVyISNF2AGOiTreZ7WrVun+fPnq7S0VJLU1NQkSYrFYj2ejcViqc9drqOjQ4lEoscHgCtLnnC6dOa1Kps6nh+UPrIxrwcyYWM8HGiB8u1vf1u///3v9cwzz/T6XCTS8y/peV6v15IqKytVUFCQ+iguLg5kvQAwGDbm9UCmbLsAM7CIZ+3atdq3b59eeeUVTZo0KfV6PB6X1N1JKSz89C/b3Nzcq6uStHHjRq1bty7150QiQZGSw7jEDrnOxptfAT/YFA/7XqB4nqe1a9dq7969evnll1VSUtLj8yUlJYrH4zpw4IBmzZolSbpw4YJqa2tVVVXV578zGo0qGuU/dBcwvAknWHjzK+AXWy7A9D3iWbNmjXbt2qXdu3crPz9fTU1Nampq0vnz5yV1Rzvl5eWqqKjQ3r17dfz4cT300EPKy8vT8uXL/V4OLMIldnCFjTe/Aq7xvYOybds2SdLChQt7vL5jxw499NBDkqT169fr/PnzWr16tVpbWzV37lzt379f+fn5fi8HlmDXA1zCib1A8AKJeAYSiUS0adMmbdq0ye//e1gqnV0PNrQWgSuxcUsm4Bdb5gQDPwcFkNj1ALckt2Q+vOuoIuo5asKJvchlNs0JclkgjKAlDtfYtiUTyJRtc4J0UGAELXG46M7SQt02Paan697VyQ8/0uRxeXqwbIpGjeB3P+QWG+cEKVBgBC1xuKivdviTBxvZNo+cY+OcIGU+jKElDpfY1g4HMmHjnCAdFBhl0ymFwFDZ2A4HMmHjnCAFCoyz5ZRCYKhsbIcDmbBxTpCIBwDSZGM7HMhE6G4zBgAX2dgOBzJl25wgEQ8ApGn25Gs0LCJ1XeHg7GGR7ufgH1tOOHWZTXOCFCgAkKb6k61XLE6k7uKl/mQrMyg+semEU9fZMidIxAMAaWIGxSy2dIcTBQoApIkZFHMG2tItdW/p7hyopYWcQ4ECAGlKbsm8kkKubvBFOlu64RYKFABI0/BhEX3p5ivPPXzp5kIGOH1AnBZeFCgAkKbOLk/7fnfluYd9vztD7OAD4rTwokABgDQNFDtIxA5+ScZp/fWiIiJOcxUFCgCkidjBnOQJp/31ojxxE7qrKFAAIE3EDkDwKFAAIE3EDuYktxn3J3lzNPM+/uns8lR3okXPHjutuhMtWXtvOUkWANKUjB0e3nVUEalH/JCti9Vcxc3RZtl0Yi8dFAAYAtsuVnMV8z7m2HZiLx0UABgimy5WcxXzPmYMdGJvMkpbNCNu7OubAgUAMmDLxWquSs77NLV93OcPz4i6u1bM+2TGxiiNiAcAYC22GZthY5RGgQIAQMjZGKVRoFzClq1VAIBubDM2w8at88ygXGTT1ioAQDcbZyNcZOPWeToosm9rFQCgm42zEa6ybet86DsoNm6tApA7Ors8thkHyMbZCJfZtHU+9AUK7UMAQ0U0HDy2GZtny9b50Ec8tA8BDAXRsBnJ2QhJvQY4uVbAbaEvUGgfAkjXQNGwxM4SP9k2GwEzQh/x0D40j8weuY5o2DybZiNgRugLFBu3VrmMzB4uIBrODltmI2BG6CMeifahKWT2cAXRMBC80HdQkmgfBovt3HAJ0TAQPDool0i2D5fOvFZlU8fzg9JH6WT2gO3YWQIEjwIFRpDZwzVEw0CwiHhgxIQxUV+fA2xANAwEhwIFZgz2+zXf15Fj2FliDkcUhAsFCoz44GyHr88BCBeOKAgfZlBgBNsyAQwVRxSEEwUKjEhuy+yvGRtR929DbMsEcCmuFTCvs8tT3YkWPXvstOpOtGTtvSXigRGc2AtgKLhWwCybojQ6KDCGbZkA0sURBebYFqXRQbkEE+LBY1smgHQwv2aGjad9U6BcZFNby3VsywQwWFwrYIaNURoRj+xrawEAunGtgBk2RmmhL1CYEAcAuzG/Fjwbo7TQRzw2trUAAD0xvxYsG6O00BcoNra1AAC9Mb8WnGSUtmrX0T4/78l8lBb6iMfGthYAAGEX+gKFE07Ns+WUQgBAt+Q8Zn+S24xNfr8OfcTDCadmsZ0bAOxj4zxm6DsoEhPiprCdGwDsZOM8Zug7KEl3lhbqtukxPV33rk5++JEmj8vTg2VTNGoENZwfbDylEADQzcZ5zKz+9P33f/93lZSU6KqrrtLs2bP1X//1X1lby/PHz2jBj17SP/3nG3qq7qT+6T/f0IIfvcRv9T5Jp30IADDLxnnMrBUoP//5z1VeXq7HHntMv/3tb/WFL3xBd911l9577z3jayF6CJ6N7UMAQDcbT+zNWoGyZcsWffOb39Rf/dVf6frrr9fWrVtVXFysbdu2GV0HJ8maYWP7EADwKdvmMbMyg3LhwgXV19fre9/7Xo/XFy9erEOHDvV6vqOjQx0dHak/JxIJ39Zi4+Syi2w8pRAA0JNNJ/ZmpYPywQcfqLOzU7FYrMfrsVhMTU1NvZ6vrKxUQUFB6qO4uNi3tRA9mGFj+xAA0FvyxN6lM69V2dTxWfu+nNUh2Uik51/a87xer0nSxo0b1dbWlvo4deqUb2sgejDHtvYhAMBeWYl4JkyYoOHDh/fqljQ3N/fqqkhSNBpVNBoNZC1ED2bZ1D4EANgrKx2UUaNGafbs2Tpw4ECP1w8cOKB58+YZXQvRg3m2tA8BAPbKWsSzbt06Pfnkk/qP//gPvfHGG3r00Uf13nvvadWqVcbXQvQAAIBdsnaS7H333aeWlhY9/vjjOnPmjEpLS/XrX/9akydPzsp6iB4AALBHxPO8nDvgI5FIqKCgQG1tbRo7dmy2lwMAAAYhnZ/fXDQDAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsQ4ECAACsk7Wj7jORPPw2kUhkeSUAAGCwkj+3B3OIfU4WKO3t7ZKk4uLiLK8EAACkq729XQUFBVd8Jifv4unq6tL777+v/Px8RSL+XuaXSCRUXFysU6dOcc9PgHifzeB9NoP32RzeazOCep89z1N7e7uKioo0bNiVp0xysoMybNgwTZo0KdD/j7Fjx/LFbwDvsxm8z2bwPpvDe21GEO/zQJ2TJIZkAQCAdShQAACAdShQLhONRvX9739f0Wg020txGu+zGbzPZvA+m8N7bYYN73NODskCAAC30UEBAADWoUABAADWoUABAADWoUABAADWoUC56JVXXtGSJUtUVFSkSCSiX/7yl9lekpMqKyt1yy23KD8/XxMnTtQ999yjN998M9vLcs62bdt00003pQ5ZKisr03PPPZftZTmvsrJSkUhE5eXl2V6KUzZt2qRIJNLjIx6PZ3tZTjp9+rQeeOABjR8/Xnl5eZo5c6bq6+uzshYKlIvOnTunm2++WdXV1dleitNqa2u1Zs0aHT58WAcOHNAnn3yixYsX69y5c9lemlMmTZqkH/7wh3r11Vf16quv6rbbbtPSpUvV0NCQ7aU568iRI9q+fbtuuummbC/FSTfccIPOnDmT+njttdeyvSTntLa26tZbb9XIkSP13HPP6fXXX9ePf/xjfeYzn8nKenLyqPsg3HXXXbrrrruyvQznPf/88z3+vGPHDk2cOFH19fX64he/mKVVuWfJkiU9/vyDH/xA27Zt0+HDh3XDDTdkaVXuOnv2rO6//37V1NToiSeeyPZynDRixAi6JgGrqqpScXGxduzYkXptypQpWVsPHRRkVVtbmyRp3LhxWV6Juzo7O7Vnzx6dO3dOZWVl2V6Ok9asWaO7775bd9xxR7aX4qw//OEPKioqUklJib761a/qnXfeyfaSnLNv3z7NmTNHX/7ylzVx4kTNmjVLNTU1WVsPBQqyxvM8rVu3TvPnz1dpaWm2l+Oc1157TVdffbWi0ahWrVqlvXv3asaMGdlelnP27Nmjo0ePqrKyMttLcdbcuXP11FNP6YUXXlBNTY2ampo0b948tbS0ZHtpTnnnnXe0bds2TZs2TS+88IJWrVql73znO3rqqaeysh4iHmTNt7/9bf3+97/XwYMHs70UJ/3Jn/yJjh07pv/93//VL37xC61YsUK1tbUUKT46deqUHnnkEe3fv19XXXVVtpfjrEvj9xtvvFFlZWWaOnWqdu7cqXXr1mVxZW7p6urSnDlzVFFRIUmaNWuWGhoatG3bNn3ta18zvh46KMiKtWvXat++fXrppZc0adKkbC/HSaNGjdIf/dEfac6cOaqsrNTNN9+sf/3Xf832spxSX1+v5uZmzZ49WyNGjNCIESNUW1urf/u3f9OIESPU2dmZ7SU6acyYMbrxxhv1hz/8IdtLcUphYWGvX2Cuv/56vffee1lZDx0UGOV5ntauXau9e/fq5ZdfVklJSbaXFBqe56mjoyPby3DK7bff3ms3yde//nVNnz5dGzZs0PDhw7O0Mrd1dHTojTfe0Be+8IVsL8Upt956a69jH9566y1Nnjw5K+uhQLno7Nmzevvtt1N/bmxs1LFjxzRu3Dhdd911WVyZW9asWaPdu3fr2WefVX5+vpqamiRJBQUFGj16dJZX546//du/1V133aXi4mK1t7drz549evnll3vtokJm8vPze81PjRkzRuPHj2euykff/e53tWTJEl133XVqbm7WE088oUQioRUrVmR7aU559NFHNW/ePFVUVOgrX/mKfvOb32j79u3avn17dhbkwfM8z3vppZc8Sb0+VqxYke2lOaWv91iSt2PHjmwvzSnf+MY3vMmTJ3ujRo3yPvvZz3q33367t3///mwvKxQWLFjgPfLII9lehlPuu+8+r7Cw0Bs5cqRXVFTk3XvvvV5DQ0O2l+WkX/3qV15paakXjUa96dOne9u3b8/aWiKe53nZKY0AAAD6xpAsAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwDgUKAACwzv8DPZ+Ih8jCARAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_df = new_df.sort_values(by=\"value\")\n",
+    "x = new_df[\"value\"]\n",
+    "y = new_df[\"roll\"]\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nEven though the values should be equally distributed, it seems like 6 appears significantly more often than 5 or 3 \\n'"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Even though the values should be equally distributed, it seems like 6 appears significantly more often than 5 or 3 \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary_stats(new_df, \"value\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   value  Count\n",
+       "0      1     12\n",
+       "1      2     17\n",
+       "2      3     14\n",
+       "3      4     22\n",
+       "4      5     12\n",
+       "5      6     23"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_df = new_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_df.rename(columns={\"roll\":\"Count\"},inplace=True)\n",
+    "sorted_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGwCAYAAABcnuQpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAitUlEQVR4nO3dfVSUdf7/8dckMIKiBorAisQqpqlZSrliKWpaWB5vatPMvKNNT2oaetylzp7Y/ZqUHsk6niytUM93vVm7MWvzrrwttfUms1xTVHJQYTmQgaCCyvX7o9P8vniLw8A1n+n5OOc6x+uai5k3o+mzz1zDOCzLsgQAAGCoW+weAAAAoCaIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYLcDuAWpbZWWlTp06pdDQUDkcDrvHAQAA1WBZls6cOaPo6Gjdcsv11178PmZOnTqlmJgYu8cAAAAeyM3NVYsWLa57jt/HTGhoqKRfnoxGjRrZPA0AAKiOkpISxcTEuP8dvx6/j5lfX1pq1KgRMQMAgGGqc4kIFwADAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoAXYPAACAP3O5XCosLLR7jFrTtGlTtWzZ0tYZiBkAAGqJy+XS7W3b6fy5s3aPUmvqB4fo0A8HbQ0aYgYAgFpSWFio8+fOKvyRqQoMj7F7HK+7UJSrok/nqLCwkJgBAMCfBYbHyBnZ2u4x/BYXAAMAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAan80EwGgul0uFhYV2j1FrmjZtausH+AEmIGYAGMvlcun2tu10/txZu0epNfWDQ3Toh4MEDXAdxAwAYxUWFur8ubMKf2SqAsNj7B7H6y4U5aro0zkqLCwkZoDrIGYAGC8wPEbOyNZ2jwHAJlwADAAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMJqtMZORkaF77rlHoaGhioiI0KBBg3To0KEq51iWpfT0dEVHRys4OFhJSUk6cOCATRMDAABfY2vMbNmyRRMmTNDOnTu1YcMGXbx4Uf369VNZWZn7nFmzZikzM1Pz5s3Trl27FBkZqb59++rMmTM2Tg4AAHxFgJ0Pvnbt2ir7WVlZioiI0J49e9SjRw9ZlqW5c+fqxRdf1JAhQyRJixcvVvPmzbV06VKNGzfOjrEBAIAP8alrZoqLiyVJYWFhkqScnBzl5+erX79+7nOcTqd69uyp7du3X/U+ysvLVVJSUmUDAAD+y2dixrIspaam6r777lOHDh0kSfn5+ZKk5s2bVzm3efPm7tsul5GRocaNG7u3mJiY2h0cAADYymdiZuLEidq/f7+WLVt2xW0Oh6PKvmVZVxz7VVpamoqLi91bbm5urcwLAAB8g63XzPxq0qRJWr16tbZu3aoWLVq4j0dGRkr6ZYUmKirKfbygoOCK1ZpfOZ1OOZ3O2h0YAAD4DFtXZizL0sSJE/Xhhx9q48aNiouLq3J7XFycIiMjtWHDBvexiooKbdmyRYmJiXU9LgAA8EG2rsxMmDBBS5cu1ccff6zQ0FD3dTCNGzdWcHCwHA6HpkyZopkzZyo+Pl7x8fGaOXOmQkJCNHz4cDtHBwAAPsLWmJk/f74kKSkpqcrxrKwsjR49WpI0ffp0nTt3Ts8++6xOnz6trl27av369QoNDa3jaQEAgC+yNWYsy7rhOQ6HQ+np6UpPT6/9gQAAgHF85t1MAAAAniBmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0QLsHgC+zeVyqbCw0O4xak3Tpk3VsmVLu8cAANQAMYNrcrlcur1tO50/d9buUWpN/eAQHfrhIEEDAAYjZnBNhYWFOn/urMIfmarA8Bi7x/G6C0W5Kvp0jgoLC4kZADAYMYMbCgyPkTOytd1jAABwVVwADAAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMJqtMbN161YNGDBA0dHRcjgcWrVqVZXbR48eLYfDUWX7wx/+YM+wAADAJ9kaM2VlZerUqZPmzZt3zXMeeugh5eXlubfPPvusDicEAAC+LsDOB09OTlZycvJ1z3E6nYqMjKz2fZaXl6u8vNy9X1JS4vF8AADA9/n8NTObN29WRESE2rRpoz/96U8qKCi47vkZGRlq3Lixe4uJiamjSQEAgB18OmaSk5P1j3/8Qxs3btScOXO0a9cu9e7du8rKy+XS0tJUXFzs3nJzc+twYgAAUNdsfZnpRoYOHer+dYcOHZSQkKDY2Fj961//0pAhQ676NU6nU06ns65GBAAANvPplZnLRUVFKTY2VtnZ2XaPAgAAfIRRMVNUVKTc3FxFRUXZPQoAAPARtr7MVFpaqiNHjrj3c3JytG/fPoWFhSksLEzp6el69NFHFRUVpR9//FEvvPCCmjZtqsGDB9s4NQAA8CW2xszu3bvVq1cv935qaqokadSoUZo/f76+++47LVmyRD///LOioqLUq1cvrVixQqGhoXaNDAAAfIytMZOUlCTLsq55+7p16+pwGgAAYCKjrpkBAAC4HDEDAACMRswAAACjETMAAMBoxAwAADCaRzGTk5Pj7TkAAAA84tFbs1u3bq0ePXooJSVFjz32mOrXr+/tuQB4icvlUmFhod1j1IqDBw/aPQIAH+BRzHz77bd67733NHXqVE2cOFFDhw5VSkqK7r33Xm/PB6AGXC6Xbm/bTufPnbV7FACoNR7FTIcOHZSZmalZs2bpk08+0aJFi3TfffcpPj5eKSkpeuqpp9SsWTNvzwrgJhUWFur8ubMKf2SqAsNj7B7H684d263ibf9r9xgAbFajnwAcEBCgwYMHq3///nrzzTeVlpamadOmKS0tTUOHDtWrr77Kh0ICPiAwPEbOyNZ2j+F1F4py7R4BgA+o0buZdu/erWeffVZRUVHKzMzUtGnTdPToUW3cuFEnT57UwIEDvTUnAADAVXm0MpOZmamsrCwdOnRI/fv315IlS9S/f3/dcssvbRQXF6e3335bbdu29eqwAAAAl/MoZubPn6+xY8dqzJgxioyMvOo5LVu21Lvvvluj4QAAAG7Eo5jJzs6+4TlBQUEaNWqUJ3cPAABQbR5dM5OVlaWVK1decXzlypVavHhxjYcCAACoLo9i5pVXXlHTpk2vOB4REaGZM2fWeCgAAIDq8ihmjh8/rri4uCuOx8bGyuVy1XgoAACA6vIoZiIiIrR///4rjn/77bcKDw+v8VAAAADV5VHMDBs2TM8995w2bdqkS5cu6dKlS9q4caMmT56sYcOGeXtGAACAa/Lo3UwzZszQ8ePH1adPHwUE/HIXlZWVGjlyJNfMAACAOuVRzAQFBWnFihX6n//5H3377bcKDg5Wx44dFRsb6+35AAAArqtGn83Upk0btWnTxluzAAAA3DSPYubSpUtatGiRvvjiCxUUFKiysrLK7Rs3bvTKcAAAADfiUcxMnjxZixYt0sMPP6wOHTrI4XB4ey4AAIBq8Shmli9frn/+85/q37+/t+cBAAC4KR69NTsoKEitW7f29iwAAAA3zaOYmTp1ql5//XVZluXteQAAAG6KRy8zffnll9q0aZPWrFmj9u3bKzAwsMrtH374oVeGAwAAuBGPYqZJkyYaPHiwt2cBAAC4aR7FTFZWlrfnAAAA8IhH18xI0sWLF/X555/r7bff1pkzZyRJp06dUmlpqdeGAwAAuBGPVmaOHz+uhx56SC6XS+Xl5erbt69CQ0M1a9YsnT9/Xm+99Za35wQAALgqj1ZmJk+erISEBJ0+fVrBwcHu44MHD9YXX3zhteEAAABuxON3M3311VcKCgqqcjw2NlYnT570ymAAAADV4dHKTGVlpS5dunTF8RMnTig0NLTGQwEAAFSXRzHTt29fzZ07173vcDhUWlqql156iY84AAAAdcqjl5lee+019erVS3fccYfOnz+v4cOHKzs7W02bNtWyZcu8PSMAAMA1eRQz0dHR2rdvn5YtW6a9e/eqsrJSKSkpevLJJ6tcEPxb4HK5VFhYaPcYteLgwYN2jwDgN4C/R1FTHsWMJAUHB2vs2LEaO3asN+cxisvl0u1t2+n8ubN2jwIARuLvUXiDRzGzZMmS694+cuRIj4YxTWFhoc6fO6vwR6YqMDzG7nG87tyx3Sre9r92jwHAj/H3KLzBo5iZPHlylf0LFy7o7NmzCgoKUkhIyG8mZn4VGB4jZ2Rru8fwugtFuXaPAOA3gr9HURMevZvp9OnTVbbS0lIdOnRI9913HxcAAwCAOuXxZzNdLj4+Xq+88soVqzYAAAC1yWsxI0n16tXTqVOnvHmXAAAA1+XRNTOrV6+usm9ZlvLy8jRv3jx1797dK4MBAABUh0cxM2jQoCr7DodDzZo1U+/evTVnzhxvzAUAAFAtHsVMZWWlt+cAAADwiFevmQEAAKhrHq3MpKamVvvczMxMTx4CAACgWjyKmW+++UZ79+7VxYsXdfvtt0uSDh8+rHr16qlz587u8xwOh3emBAAAuAaPYmbAgAEKDQ3V4sWLdeutt0r65QfpjRkzRvfff7+mTp3q1SEBAACuxaNrZubMmaOMjAx3yEjSrbfeqhkzZvBuJgAAUKc8ipmSkhL997//veJ4QUGBzpw5U+OhAAAAqsujmBk8eLDGjBmj999/XydOnNCJEyf0/vvvKyUlRUOGDPH2jAAAANfk0TUzb731lqZNm6YRI0bowoULv9xRQIBSUlI0e/Zsrw4IAABwPR7FTEhIiN58803Nnj1bR48elWVZat26tRo0aODt+QAAAK6rRj80Ly8vT3l5eWrTpo0aNGggy7K8NRcAAEC1eBQzRUVF6tOnj9q0aaP+/fsrLy9PkvT000/ztmwAAFCnPIqZ559/XoGBgXK5XAoJCXEfHzp0qNauXeu14QAAAG7Eo2tm1q9fr3Xr1qlFixZVjsfHx+v48eNeGQwAAKA6PFqZKSsrq7Ii86vCwkI5nc4aDwUAAFBdHsVMjx49tGTJEve+w+FQZWWlZs+erV69enltOAAAgBvx6GWm2bNnKykpSbt371ZFRYWmT5+uAwcO6KefftJXX33l7RkBAACuyaOVmTvuuEP79+/Xvffeq759+6qsrExDhgzRN998o1atWnl7RgAAgGu66ZWZCxcuqF+/fnr77bf1t7/9rTZmAgAAqLabXpkJDAzU999/L4fDUeMH37p1qwYMGKDo6Gg5HA6tWrWqyu2WZSk9PV3R0dEKDg5WUlKSDhw4UOPHBQAA/sOjl5lGjhypd999t8YPXlZWpk6dOmnevHlXvX3WrFnKzMzUvHnztGvXLkVGRqpv3758MjcAAHDz6ALgiooKvfPOO9qwYYMSEhKu+EymzMzMat1PcnKykpOTr3qbZVmaO3euXnzxRfcncS9evFjNmzfX0qVLNW7cOE9GBwAAfuamYubYsWO67bbb9P3336tz586SpMOHD1c5xxsvP0lSTk6O8vPz1a9fP/cxp9Opnj17avv27deMmfLycpWXl7v3S0pKvDIPAADwTTcVM/Hx8crLy9OmTZsk/fLxBW+88YaaN2/u9cHy8/Ml6Yr7bt68+XV/ynBGRgYXJgMA8BtyU9fMXP6p2GvWrFFZWZlXB7rc5Ss9lmVdd/UnLS1NxcXF7i03N7dW5wMAAPby6JqZX10eN94UGRkp6ZcVmqioKPfxgoKC664EOZ1OPlIBAIDfkJtamXE4HFesinjrGpnLxcXFKTIyUhs2bHAfq6io0JYtW5SYmFgrjwkAAMxzUyszlmVp9OjR7pWP8+fPa/z48Ve8m+nDDz+s1v2VlpbqyJEj7v2cnBzt27dPYWFhatmypaZMmaKZM2cqPj5e8fHxmjlzpkJCQjR8+PCbGRsAAPixm4qZUaNGVdkfMWJEjR589+7dVT6YMjU11f04ixYt0vTp03Xu3Dk9++yzOn36tLp27ar169crNDS0Ro8LAAD8x03FTFZWllcfPCkp6brX3TgcDqWnpys9Pd2rjwsAAPyHRz8BGAAAwFcQMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaD4dM+np6XI4HFW2yMhIu8cCAAA+JMDuAW6kffv2+vzzz9379erVs3EaAADga3w+ZgICAm5qNaa8vFzl5eXu/ZKSktoYCwAA+AiffplJkrKzsxUdHa24uDgNGzZMx44du+75GRkZaty4sXuLiYmpo0kBAIAdfDpmunbtqiVLlmjdunVauHCh8vPzlZiYqKKiomt+TVpamoqLi91bbm5uHU4MAADqmk+/zJScnOz+dceOHdWtWze1atVKixcvVmpq6lW/xul0yul01tWIAADAZj69MnO5Bg0aqGPHjsrOzrZ7FAAA4COMipny8nIdPHhQUVFRdo8CAAB8hE/HzLRp07Rlyxbl5OTo66+/1mOPPaaSkhKNGjXK7tEAAICP8OlrZk6cOKEnnnhChYWFatasmf7whz9o586dio2NtXs0AADgI3w6ZpYvX273CAAAwMf59MtMAAAAN0LMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMZkTMvPnmm4qLi1P9+vXVpUsXbdu2ze6RAACAj/D5mFmxYoWmTJmiF198Ud98843uv/9+JScny+Vy2T0aAADwAT4fM5mZmUpJSdHTTz+tdu3aae7cuYqJidH8+fPtHg0AAPiAALsHuJ6Kigrt2bNHf/nLX6oc79evn7Zv337VrykvL1d5ebl7v7i4WJJUUlLi9flKS0t/ecz8I6qsOO/1+7fbhaJcSX78/f10QpK0Z88e9++lvzl06JAkP/495M+o8fgzarZf/4yWlpZ6/d/ZX+/Psqwbn2z5sJMnT1qSrK+++qrK8Zdfftlq06bNVb/mpZdesiSxsbGxsbGx+cGWm5t7w17w6ZWZXzkcjir7lmVdcexXaWlpSk1Nde9XVlbqp59+Unh4+DW/xlMlJSWKiYlRbm6uGjVq5NX7xv/H81w3eJ7rBs9z3eB5rhu1+TxblqUzZ84oOjr6huf6dMw0bdpU9erVU35+fpXjBQUFat68+VW/xul0yul0VjnWpEmT2hpRktSoUSP+Y6kDPM91g+e5bvA81w2e57pRW89z48aNq3WeT18AHBQUpC5dumjDhg1Vjm/YsEGJiYk2TQUAAHyJT6/MSFJqaqqeeuopJSQkqFu3blqwYIFcLpfGjx9v92gAAMAH+HzMDB06VEVFRfr73/+uvLw8dejQQZ999pliY2PtHk1Op1MvvfTSFS9rwbt4nusGz3Pd4HmuGzzPdcNXnmeHZVXnPU8AAAC+yaevmQEAALgRYgYAABiNmAEAAEYjZgAAgNGIGQ9s3bpVAwYMUHR0tBwOh1atWmX3SH4nIyND99xzj0JDQxUREaFBgwa5P8MF3jV//nzdeeed7h961a1bN61Zs8busfxaRkaGHA6HpkyZYvcofic9PV0Oh6PKFhkZafdYfunkyZMaMWKEwsPDFRISorvuukt79uyxZRZixgNlZWXq1KmT5s2bZ/cofmvLli2aMGGCdu7cqQ0bNujixYvq16+fysrK7B7N77Ro0UKvvPKKdu/erd27d6t3794aOHCgDhw4YPdofmnXrl1asGCB7rzzTrtH8Vvt27dXXl6ee/vuu+/sHsnvnD59Wt27d1dgYKDWrFmj//znP5ozZ06t/8T9a/H5nzPji5KTk5WcnGz3GH5t7dq1VfazsrIUERGhPXv2qEePHjZN5Z8GDBhQZf/ll1/W/PnztXPnTrVv396mqfxTaWmpnnzySS1cuFAzZsywexy/FRAQwGpMLXv11VcVExOjrKws97HbbrvNtnlYmYERiouLJUlhYWE2T+LfLl26pOXLl6usrEzdunWzexy/M2HCBD388MN64IEH7B7Fr2VnZys6OlpxcXEaNmyYjh07ZvdIfmf16tVKSEjQH//4R0VEROjuu+/WwoULbZuHmIHPsyxLqampuu+++9ShQwe7x/FL3333nRo2bCin06nx48fro48+0h133GH3WH5l+fLl2rt3rzIyMuwexa917dpVS5Ys0bp167Rw4ULl5+crMTFRRUVFdo/mV44dO6b58+crPj5e69at0/jx4/Xcc89pyZIltszDy0zweRMnTtT+/fv15Zdf2j2K37r99tu1b98+/fzzz/rggw80atQobdmyhaDxktzcXE2ePFnr169X/fr17R7Hr/3fSwA6duyobt26qVWrVlq8eLFSU1NtnMy/VFZWKiEhQTNnzpQk3X333Tpw4IDmz5+vkSNH1vk8rMzAp02aNEmrV6/Wpk2b1KJFC7vH8VtBQUFq3bq1EhISlJGRoU6dOun111+3eyy/sWfPHhUUFKhLly4KCAhQQECAtmzZojfeeEMBAQG6dOmS3SP6rQYNGqhjx47Kzs62exS/EhUVdcX/7LRr104ul8uWeViZgU+yLEuTJk3SRx99pM2bNysuLs7ukX5TLMtSeXm53WP4jT59+lzxjpoxY8aobdu2+vOf/6x69erZNJn/Ky8v18GDB3X//ffbPYpf6d69+xU/LuPw4cO2fQg0MeOB0tJSHTlyxL2fk5Ojffv2KSwsTC1btrRxMv8xYcIELV26VB9//LFCQ0OVn58vSWrcuLGCg4Ntns6/vPDCC0pOTlZMTIzOnDmj5cuXa/PmzVe8owyeCw0NveJ6rwYNGig8PJzrwLxs2rRpGjBggFq2bKmCggLNmDFDJSUlGjVqlN2j+ZXnn39eiYmJmjlzph5//HH9+9//1oIFC7RgwQJ7BrJw0zZt2mRJumIbNWqU3aP5jas9v5KsrKwsu0fzO2PHjrViY2OtoKAgq1mzZlafPn2s9evX2z2W3+vZs6c1efJku8fwO0OHDrWioqKswMBAKzo62hoyZIh14MABu8fyS5988onVoUMHy+l0Wm3btrUWLFhg2ywOy7IsezIKAACg5rgAGAAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgaAMZKSkjRlyhS7xwDgY4gZAHViwIABeuCBB656244dO+RwOLR37946ngqAPyBmANSJlJQUbdy4UcePH7/itvfee0933XWXOnfubMNkAExHzACoE4888ogiIiK0aNGiKsfPnj2rFStWaNCgQXriiSfUokULhYSEqGPHjlq2bNl179PhcGjVqlVVjjVp0qTKY5w8eVJDhw7VrbfeqvDwcA0cOFA//vij+/bNmzfr3nvvVYMGDdSkSRN17979qsEFwHcRMwDqREBAgEaOHKlFixbp/36+7cqVK1VRUaGnn35aXbp00aeffqrvv/9ezzzzjJ566il9/fXXHj/m2bNn1atXLzVs2FBbt27Vl19+qYYNG+qhhx5SRUWFLl68qEGDBqlnz57av3+/duzYoWeeeUYOh8Mb3zKAOhJg9wAAfjvGjh2r2bNna/PmzerVq5ekX15iGjJkiH73u99p2rRp7nMnTZqktWvXauXKleratatHj7d8+XLdcssteuedd9yBkpWVpSZNmmjz5s1KSEhQcXGxHnnkEbVq1UqS1K5duxp+lwDqGiszAOpM27ZtlZiYqPfee0+SdPToUW3btk1jx47VpUuX9PLLL+vOO+9UeHi4GjZsqPXr18vlcnn8eHv27NGRI0cUGhqqhg0bqmHDhgoLC9P58+d19OhRhYWFafTo0XrwwQc1YMAAvf7668rLy/PWtwugjhAzAOpUSkqKPvjgA5WUlCgrK0uxsbHq06eP5syZo9dee03Tp0/Xxo0btW/fPj344IOqqKi45n05HI4qL1lJ0oULF9y/rqysVJcuXbRv374q2+HDhzV8+HBJv6zU7NixQ4mJiVqxYoXatGmjnTt31s43D6BWEDMA6tTjjz+uevXqaenSpVq8eLHGjBkjh8Ohbdu2aeDAgRoxYoQ6deqk3//+98rOzr7ufTVr1qzKSkp2drbOnj3r3u/cubOys7MVERGh1q1bV9kaN27sPu/uu+9WWlqatm/frg4dOmjp0qXe/8YB1BpiBkCdatiwoYYOHaoXXnhBp06d0ujRoyVJrVu31oYNG7R9+3YdPHhQ48aNU35+/nXvq3fv3po3b5727t2r3bt3a/z48QoMDHTf/uSTT6pp06YaOHCgtm3bppycHG3ZskWTJ0/WiRMnlJOTo7S0NO3YsUPHjx/X+vXrdfjwYa6bAQxDzACocykpKTp9+rQeeOABtWzZUpL017/+VZ07d9aDDz6opKQkRUZGatCgQde9nzlz5igmJkY9evTQ8OHDNW3aNIWEhLhvDwkJ0datW9WyZUsNGTJE7dq109ixY3Xu3Dk1atRIISEh+uGHH/Too4+qTZs2euaZZzRx4kSNGzeuNr99AF7msC5/wRkAAMAgrMwAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAw2v8DDFX6gFR6qiIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(new_df['value'], bins='auto', edgecolor='black')  \n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_df = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "dice_df.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
+    "\n",
+    "sorted_dice = dice_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_dice.rename(columns={\"roll\":\"num_of_rolls\"},inplace=True)\n",
+    "x = sorted_dice[\"value\"]\n",
+    "y = sorted_dice[\"num_of_rolls\"]\n",
+    "plt.plot(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Due to the higher amount of Rolls it seems like the graph is more equaly distributed\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 4\n",
+    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "plt.hist(age_pop['observation'], bins='auto', edgecolor='black')  \n",
+    "\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Guesses:\n",
+    "mean = 38\n",
+    "std 20\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 36.56\n",
+      "Standard Deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(f\"\"\"Mean : {age_pop[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nmean was good guess, standart deviation not really\\n'"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "mean was good guess, standart deviation not really\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation  count\n",
+       "0          28.0    139\n",
+       "1          27.0    125\n",
+       "2          26.0    120\n",
+       "3          29.0    115\n",
+       "4          25.0     98\n",
+       "5          30.0     90\n",
+       "6          24.0     78\n",
+       "7          31.0     61\n",
+       "8          23.0     41\n",
+       "9          22.0     35\n",
+       "10         32.0     31\n",
+       "11         33.0     22\n",
+       "12         21.0     17\n",
+       "13         20.0     13\n",
+       "14         34.0      7\n",
+       "15         35.0      3\n",
+       "16         19.0      3\n",
+       "17         36.0      2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "freq = age2.value_counts().reset_index()\n",
+    "freq.columns = [\"observation\", \"count\"]\n",
+    "y = freq[\"observation\"]\n",
+    "x = freq[\"count\"]\n",
+    "display(freq)\n",
+    "plt.plot(x,y,c=\"skyblue\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(age_pop2['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "shape changed a bit, more clear and also more equally distributed\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 36.56\n",
+      "Standard Deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 27.155\n",
+      "Standard Deviation: 2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop2[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop2[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "mean decreased, huge drop in the standart deviation ==> more equally distribution as i asumed\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 5\n",
+    "Now is the turn of `ages_population3.csv`.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "plt.hist(age_pop3['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 41.989\n",
+      "Standard Deviation: 16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop3[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop3[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "standart deviation increased again a lot, mean also increased a lot\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0\n",
+      "40.0\n",
+      "53.0\n",
+      "77.0\n",
+      "41.989\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop3[\"observation\"].quantile(0.25))\n",
+    "print(age_pop3[\"observation\"].quantile(0.5))\n",
+    "print(age_pop3[\"observation\"].quantile(0.75))\n",
+    "print(age_pop3[\"observation\"].quantile(1))\n",
+    "print(age_pop3[\"observation\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe difference between mean and medain is around 1.9 years. 50 % of the people in this neighborhood are between 30 and 53 \\nyears old.\\n'"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The difference between mean and medain is around 1.9 years. 50 % of the people in this neighborhood are between 30 and 53 \n",
+    "years old.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/.DS_Store b/your-code/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/your-code/.DS_Store differ
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..eb4540c 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import pearsonr, spearmanr\n",
+    "import random"
    ]
   },
   {
@@ -29,11 +35,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "def roll_dice():\n",
+    "    rolls = []\n",
+    "    my_dict = {}\n",
+    "    index = 1\n",
+    "    for i in range(10):\n",
+    "        roll = random.randint(1, 6)\n",
+    "        rolls.append(roll)  \n",
+    "        my_dict[index] = {\"Roll\" : i+1, \"Value\" : roll}\n",
+    "        index += 1\n",
+    "    df = pd.DataFrame.from_dict(my_dict, orient=\"index\")\n",
+    "  \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Roll  Value\n",
+       "1      1      2\n",
+       "2      2      4\n",
+       "3      3      6\n",
+       "4      4      2\n",
+       "5      5      3\n",
+       "6      6      3\n",
+       "7      7      5\n",
+       "8      8      4\n",
+       "9      9      2\n",
+       "10    10      5"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice()"
    ]
   },
   {
@@ -45,11 +170,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Roll  Value\n",
+       "1      1      2\n",
+       "2      2      3\n",
+       "3      3      6\n",
+       "4      4      2\n",
+       "5      5      2\n",
+       "6      6      4\n",
+       "7      7      2\n",
+       "8      8      4\n",
+       "9      9      4\n",
+       "10    10      1"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = roll_dice()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAtTUlEQVR4nO3daXiU9d328XOyTUhIBggkEBJ2kSVAErAquC9UFEQFVMDe3u1zH8/t0bAIta5t3YlL1VpBWqyPra0sCqKoBcQFEBXFkACy7wTCFpZMFjJJZq7nRQgNCsIkM/nP8v0cx7yYMWFOGZM5va7f/C6bZVmWAAAAfCDCdAAAABA6KBYAAMBnKBYAAMBnKBYAAMBnKBYAAMBnKBYAAMBnKBYAAMBnKBYAAMBnopr6CT0ej4qKipSQkCCbzdbUTw8AABrAsiyVlpYqNTVVERFnPy7R5MWiqKhI6enpTf20AADABwoLC5WWlnbWf97kxSIhIUFSbbDExMSmfnoAANAATqdT6enpp97Hz6bJi0Xd6Y/ExESKBQAAQeZcYwwMbwIAAJ+hWAAAAJ+hWAAAAJ+hWAAAAJ+hWAAAAJ+hWAAAAJ+hWAAAAJ+hWAAAAJ+hWAAAAJ/xuljs27dPd911l5KSkhQXF6fMzEzl5eX5IxsAAAgyXq30PnbsmAYNGqSrr75aCxcuVHJysrZv364WLVr4KR4AAAgmXhWLZ599Vunp6XrjjTdOPdapUydfZwIAAEHKq1MhCxYs0IABAzRq1CglJycrKytLr7322k9+j8vlktPpPO0GAAB877EF6/Xq0m06Wl5lLINXxWLHjh2aPn26LrjgAi1evFj33HOPJkyYoDfffPOs35ObmyuHw3Hqlp6e3ujQAADgdIdLXfrnyt16btFmo8XCZlmWdb5fHBMTowEDBuirr7469diECRO0atUqff3112f8HpfLJZfLdep+3fXcS0pKuGw6AAA+8rcvduipjzYqM72F3ssZ5PM/3+l0yuFwnPP926sjFu3atVOvXr1Oe6xnz57as2fPWb/HbrcrMTHxtBsAAPCtuXl7JUkj+qcZzeFVsRg0aJA2b9582mNbtmxRx44dfRoKAACcv/VFJdp0oFQxkRG6uW+q0SxeFYtJkyZp5cqVmjJlirZt26aZM2dqxowZysnJ8Vc+AABwDnVHK67vlSJHXLTRLF4Vi4suukjz58/XrFmzlJGRoSeffFJ/+tOfNHbsWH/lAwAAP6Ha7dGCgiJJ0kjDp0EkL/dYSNLQoUM1dOhQf2QBAABeWrr5sI6UV6lNgl2XX9DadByuFQIAQDCbm1coSbo1q72iIs2/rZtPAAAAGuRoeZU+23RIkjQi2/xpEIliAQBA0FpQsE/VbksZ7RN1YdsE03EkUSwAAAha81bvkySNDJCjFRLFAgCAoLT5QKnW7StRdKRNN2e2Nx3nFIoFAABBaN7q2t0V1/RIVqv4GMNp/oNiAQBAkKlxe/TuydMggTK0WYdiAQBAkPlia7GKy1xKio/R1T2STcc5DcUCAIAgM/fkaZCbM1MVHQC7K+oLrDQAAOAnlVRUa8n6g5ICY4X3D1EsAAAIIh+sLVKV26MebRPUO9VhOs6PUCwAAAgidVcyDcSjFRLFAgCAoLHtUJkKCo8rMsKm4QG0u6I+igUAAEGibnfFVd3bqE2C3XCaM6NYAAAQBNweS/PrVngH6GkQiWIBAEBQ+Gp7sQ44K+VoFq1regbW7or6KBYAAASBuqHN4ZmpskdFGk5zdhQLAAACnLOyWovXH5AUeCu8f4hiAQBAgPv32v2qrPaoW3Jz9U0LvN0V9VEsAAAIcHWfBhnZP002m81wmp9GsQAAIIDtKi7Xql3HFGGTbs0KzN0V9VEsAAAIYO+ePFpx+QVtlJIYazjNuVEsAAAIUB6PpXknd1eMCODdFfVRLAAACFArdx7RvuMnlBAbpcG9UkzHOS8UCwAAAtS8vNqjFUP7pio2OnB3V9RHsQAAIACVu2q08Pv9kqSR/QN/aLMOxQIAgAC08PsDqqhyq3PreGV3aGk6znmjWAAAEIDm5hVKkkZktw/43RX1USwAAAgwhUcrtHLHUdls0q0BvsL7hygWAAAEmHdPfsR0YNcktW/RzHAa71AsAAAIIJZl6d38/6zwDjYUCwAAAsh3u49p95EKxcdE6ue925qO4zWKBQAAAWTud7VHK27s005xMVGG03iPYgEAQIA4UeXWR+vqdlcE32kQiWIBAEDAWLz+gMpcNUpv1UwXdWplOk6DUCwAAAgQ805eyXREdpoiIoJnd0V9FAsAAAJA0fETWrGtWFJtsQhWFAsAAALA/Px9sizp4s6tlN4qznScBqNYAABgmGVZ/zkNEqRDm3UoFgAAGJZfeFw7DperWXSkbuzTznScRqFYAABg2Ly82qMVQzLaqrk9+HZX1EexAADAoMpqtz5YUyQp+E+DSBQLAACM+mTjQTkra5TqiNWlXZJMx2k0igUAAAbNPXka5LYg3l1RH8UCAABDDjkrtXzLYUmhcRpEolgAAGDMewX75LGk/h1bqnPreNNxfIJiAQCAAZZlnToNEsybNn+IYgEAgAHf73Nqy8Ey2aMidFPf4N5dUR/FAgAAA+bmFUqSBvduK0ezaMNpfIdiAQBAE3PVuPX+yd0VI0NkaLMOxQIAgCb2+aZDOl5RrZREuy7r1tp0HJ+iWAAA0MTm5u2TJN2alabIENhdUR/FAgCAJlRc5tLSzYckSSP7tzecxvcoFgAANKH3C4pU47HUL82hbskJpuP4HMUCAIAmVHcl01Ab2qxDsQAAoIlsKHJqw36nYiIjNKxfquk4fkGxAACgicxbXXu04rpeyWoRF2M4jX94VSwee+wx2Wy2025t27b1VzYAAEJGtduj9/JrPw0SSiu8fyjK22/o3bu3Pvnkk1P3IyMjfRoIAIBQtGzzYR0pr1Lr5nZd0b2N6Th+43WxiIqK4igFAABeqrvg2C2ZqYqODN1JBK//zbZu3arU1FR17txZd955p3bs2PGTX+9yueR0Ok+7AQAQTo6VV+nTTQclSSNC9NMgdbwqFhdffLHefPNNLV68WK+99poOHDiggQMH6siRI2f9ntzcXDkcjlO39PT0RocGACCYfLC2SNVuS71TE9WzXaLpOH5lsyzLaug3l5eXq2vXrrr//vs1efLkM36Ny+WSy+U6dd/pdCo9PV0lJSVKTAztv1wAACTp5qkrtHZvif4wtJd+dVln03EaxOl0yuFwnPP92+sZi/ri4+PVp08fbd269axfY7fbZbfbG/M0AAAErS0HS7V2b4miImwanhmauyvqa9T0iMvl0saNG9WuXTtf5QEAIKTUbdq8ukeykpqH/v9oe1Us7rvvPi1btkw7d+7UN998o5EjR8rpdOruu+/2Vz4AAIJWjduj+Sd3V4TqCu8f8upUyN69ezV69GgVFxerTZs2uuSSS7Ry5Up17NjRX/kAAAhaX2wr1qFSl1rGRevqC5NNx2kSXhWL2bNn+ysHAAAhp+40yPDM9oqJCt3dFfWFx78lAABNrKSiWh9vqN1dES6nQSSKBQAAfvHhuiJV1Xh0YUqCeqeGz3oFigUAAH5QdxpkZP802Ww2w2maDsUCAAAf2364TKv3HFdkhE3Ds0J/d0V9FAsAAHzs3dW1Ryuu7N5GyQmxhtM0LYoFAAA+5PZYend17e6KEdnhM7RZh2IBAIAPfb39iPaXVMrRLFrX9gyP3RX1USwAAPChuXmFkqRh/dopNjrScJqmR7EAAMBHSiurtWj9AUnheRpEolgAAOAzC9cdUGW1R13bxCszvYXpOEZQLAAA8JG5J3dXjAiz3RX1USwAAPCB3UfK9e2uo4qwSbdlhedpEIliAQCAT8w7+RHTQd1aq60jvHZX1EexAACgkTwe69RSrHC64NiZUCwAAGikb3Ye1d5jJ5Rgj9LPe7c1HccoigUAAI007+TRipv6hufuivooFgAANEK5q0b/XrdfEqdBJIoFAACNsuj7A6qocqtTUpz6d2xpOo5xFAsAABqh7jTIiOzw3V1RH8UCAIAG2nusQl9tPyJJujW7veE0gYFiAQBAA80/ubtiYNckpbWMM5wmMFAsAABoAMuyTjsNgloUCwAAGiBv9zHtOlKhuJhI3ZAR3rsr6qNYAADQAHUXHLuxTzvF26MMpwkcFAsAALxUWe3WR2trd1dwGuR0FAsAALy0eP0BlbpqlNaymS7u3Mp0nIBCsQAAwEt1p0Fuy05TRAS7K+qjWAAA4IUDJZX6cluxJGkEuyt+hGIBAIAX3s3fK48l/axTK3VMijcdJ+BQLAAAOE+WZWneydMgI/pztOJMKBYAAJyngsLj2n64XLHREbqxTzvTcQISxQIAgPNUt2nzht5tlRAbbThNYKJYAABwHiqr3fpgTe3uipH90w2nCVwUCwAAzsOnGw+p5ES12jlidWnXJNNxAhbFAgCA81B3GuS27PaKZHfFWVEsAAA4h0OllVq25bCk2qVYODuKBQAA5/B+fpHcHktZHVqoa5vmpuMENIoFAAA/wbKsUyu8R/bnaMW5UCwAAPgJ64uc2nywVDFRERraN9V0nIBHsQAA4CfUHa0Y3CtFjmbsrjgXigUAAGdRVePR+wX7JEkjOA1yXigWAACcxeebD+lYRbWSE+y6vFtr03GCAsUCAICzqDsNcmtWe0VF8pZ5PvhbAgDgDI6UufT5pkOSOA3iDYoFAABn8H5BkWo8lvqmOdQ9JcF0nKBBsQAA4AzqVniPYNOmVygWAAD8wMb9Tq0vcio60qab+7G7whsUCwAAfmDeyaHNa3ukqGV8jOE0wYViAQBAPdVuj94rKJLECu+GoFgAAFDP8i2HVVzmUlJ8jK68sI3pOEGHYgEAQD11Q5u3ZLVXNLsrvMbfGAAAJx2vqNInG07uruDTIA1CsQAA4KQP1hSpyu1Rz3aJ6pWaaDpOUKJYAABwUt0Kb4Y2G45iAQCApG2HSrVmb4miImwansnuioaiWAAAIGluXu3l0a+6MFmtm9sNpwlejSoWubm5stlsuvfee30UBwCApuf2WJqfX3capL3hNMGtwcVi1apVmjFjhvr27evLPAAANLkV24p10OlSy7hoXdMjxXScoBbVkG8qKyvT2LFj9dprr+mpp57ydSYACHnHK6p0uNRlOgZOemvlbknSzf1SFRPFlEBjNKhY5OTk6KabbtJ11113zmLhcrnkcv3nh8fpdDbkKQEgZCzbclj3/DNPJ6rdpqPgB0bwaZBG87pYzJ49W6tXr9aqVavO6+tzc3P1+OOPex0MAELRgZJKTZpToBPVbiXYoxTN/x0HjMsvaK0+7R2mYwQ9r4pFYWGhJk6cqI8//lixsbHn9T0PPfSQJk+efOq+0+lUenq6dykBIATUuD2aMDtfR8ur1Ktdot799UDFRkeajgX4lFfFIi8vT4cOHVL//v1PPeZ2u7V8+XJNnTpVLpdLkZGn/5DY7XbZ7XxsBwD+/OlWfbvzqOJjIjVtbDalAiHJq2Jx7bXXat26dac99stf/lI9evTQAw888KNSAQCotWJrsV75fJskKXdEX3VuHW84EeAfXhWLhIQEZWRknPZYfHy8kpKSfvQ4AKDWodJK3TunQJYljf5ZB93cj62OCF1MDQGAH7k9lu6dXaDiMpd6tE3Qo8N6mY4E+FWDPm5a39KlS30QAwBC09TPtumr7UcUFxOpqWOYq0Do44gFAPjJ19uP6OVPt0iSnrolQ92SmxtOBPgfxQIA/KC4zKWJs/PlsaRR/dN0WzaLlxAeKBYA4GMej6VJcwp0qNSlC5Kb6/HhvU1HApoMxQIAfGz6su36YmuxYqMjNG1stuJiGj3OBgQNigUA+NCqXUf14pLauYonhmeoe0qC4URA06JYAICPHC2v0viZ+XJ7LN2a1V6juKAVwhDFAgB8wOOx9Ju3C3TAWakubeL11C0ZstlspmMBTY5iAQA+8NoXO/T55sOyR0Vo2phsxduZq0B4olgAQCPl7T6m5xZvliQ9Oqy3erZLNJwIMIdiAQCNcLyiShNm1c5VDOuXqtE/SzcdCTCKYgEADWRZlu57Z632HT+hTklxmnIrcxUAxQIAGuj/fblLn2w8qJjICE0dk62E2GjTkQDjKBYA0ABrCo/rmYUbJUm/G9pTGe0dhhMBgYFiAQBeKjlRrZyZq1XttnRjn7b6xSUdTUcCAgbFAgC8YFmWHpi7VnuPnVB6q2Z6ZkRf5iqAeigWAOCFN7/erUXrDyg60qZpY7KVyFwFcBqKBQCcp+/3lejpj2rnKh4a0lN901qYDQQEIIoFAJyH0srauYoqt0fX90rRLwd1Mh0JCEgUCwA4B8uy9NC767T7SIXat2im50cyVwGcDcUCAM5h5rd79OHa/YqKsOmVMVlqERdjOhIQsCgWAPATNhQ59fgHGyRJ999wobI7tDScCAhsFAsAOIsyV43GzVytqhqPrumRrP+5rIvpSEDAo1gAwBlYlqXfzV+nHcXlaueI1Quj+ikigrkK4FwoFgBwBu98t1fvFRQpMsKmV0ZnqWU8cxXA+aBYAMAPbDlYqj8s+F6S9JvB3TWgUyvDiYDgQbEAgHoqqmr067dWq7Laoyu6t9E9V3Q1HQkIKhQLAKjnD++v17ZDZUpOsOvF25mrALxFsQCAk+bl7dXcvL2KsEl/Hp2l1s3tpiMBQYdiAQCSth0q1e/eq52ruPe67rqkS5LhREBwolgACHsnqtzKeStfJ6rdGtQtSTlXdzMdCQhaFAsAYe+JD9dr88FStW5u10t3ZCqSuQqgwSgWAMLa+wX7NOvbQtls0st3Zio5IdZ0JCCoUSwAhK0dh8v08LvrJEnjr+6mQd1aG04EBD+KBYCwVFntVs7MfJVXuXVx51aaeF1305GAkECxABCWnvpogzbudyopPkZ/Hp3FXAXgIxQLAGHnw7VF+tfKPZKkF+/IVEoicxWAr1AsAISV3UfK9dC82rmKX1/VVVd2b2M4ERBaKBYAwoarxq1xM/NV6qrRgI4tNfl65ioAX6NYAAgbuf/epHX7StQiLlp/Hp2lqEh+BQK+xk8VgLCw6PsD+vtXuyRJL97eT6ktmpkNBIQoigWAkFd4tEL3z10jSfq/V3TRNT1SDCcCQhfFAkBIq6rxaNysfDkra5TVoYV++/MLTUcCQhrFAkBIe27RJq0pPC5Hs2i9MjpL0cxVAH7FTxiAkPXJhoP624qdkqTnR/ZVWss4w4mA0EexABCSio6f0H0n5yp+NaizBvduazgREB4oFgBCTrXbo/Gz8nW8olp90xx6cEgP05GAsEGxABByXvh4i/J2H1OCPUpTR2crJopfdUBT4acNQEj5fPMh/WXZdknScyP7qkMScxVAU6JYAAgZB0oq9Zu3a+cq/uvSjhrSp53hRED4oVgACAk1bo8mzMrX0fIq9U5N1MM39jQdCQhLFAsAIeHlT7fq211H1dwepWljshUbHWk6EhCWKBYAgt6KrcWa+vk2SdKU2/qoU+t4w4mA8EWxABDUDjkrde+cfFmWNPpnHXRzv1TTkYCwRrEAELTcHksTZxeouKxKPdom6NFhvUxHAsIexQJA0Hrls636escRxcVEatpY5iqAQECxABCUvtperJc/3SpJevrWDHVt09xwIgCSl8Vi+vTp6tu3rxITE5WYmKhLL71UCxcu9Fc2ADijw6UuTZxdIMuSbh+Qpluz0kxHAnCSV8UiLS1NzzzzjL777jt99913uuaaazR8+HCtX7/eX/kA4DQej6XJbxfocKlLFyQ31+M3Z5iOBKCeKG++eNiwYafdf/rppzV9+nStXLlSvXv39mkwADiT6cu264utxYqNjtC0sdlqFsNcBRBIvCoW9bndbr3zzjsqLy/XpZdeetavc7lccrlcp+47nc6GPiWAMPftzqN64ePNkqQnhmeoe0qC4UQAfsjr4c1169apefPmstvtuueeezR//nz16nX2j3jl5ubK4XCcuqWnpzcqMIDwdLS8ShNm5ctjSbdltdeo/sxVAIHIZlmW5c03VFVVac+ePTp+/LjmzZunv/3tb1q2bNlZy8WZjlikp6erpKREiYmJjUsPICx4PJZ+9Y9VWrr5sLq2ideCcZcp3t7gA64AGsDpdMrhcJzz/dvrn8yYmBh169ZNkjRgwACtWrVKL7/8sv7617+e8evtdrvsdru3TwMAp8z4YoeWbj4se1TtXAWlAghcjd5jYVnWaUckAMCX8nYf1fOLa+cqHru5t3q05UgnEMi8qv0PP/ywhgwZovT0dJWWlmr27NlaunSpFi1a5K98AMLYsfIqjZ+ZL7fH0rB+qbrzIma0gEDnVbE4ePCgfvGLX2j//v1yOBzq27evFi1apOuvv95f+QCEKcuy9Nu5a1RUUqlOSXGacmuGbDab6VgAzsGrYvH666/7KwcAnOb1FTv1ycZDiomM0NQx2UqIjTYdCcB54FohAAJOQeFxPbtokyTp90N7KqO9w3AiAOeLYgEgoJScqNa4matV7bZ0Y5+2uuuSjqYjAfACxQJAwLAsS/fPXaO9x06oQ6s4PTOiL3MVQJChWAAIGP/4apcWrz+o6Eibpo7JUiJzFUDQoVgACAjr9pZoyr9r5yoeGtJTfdNamA0EoEEoFgCMc1ZWK2fmalW5PRrcK0W/HNTJdCQADUSxAGCUZVl66N112nO0Qu1bNNPzI/sxVwEEMYoFAKPe+maPPlq7X1ERtXMVjjjmKoBgRrEAYMz6ohI98eEGSdIDN/RQVoeWhhMBaCyKBQAjylw1GjczX1U1Hl3bI1n/c3ln05EA+ADFAkCTsyxLj8xfp53F5WrniNUfRzFXAYQKigWAJjdnVaHeLyhSZIRNr4zOUsv4GNORAPgIxQJAk9p0wKlHF6yXJP1mcHcN6NTKcCIAvkSxANBkKqpqlPPWarlqPLqyexvdc0VX05EA+BjFAkCT+f1767X9cLlSEu168fZ+iohgrgIINRQLAE1ibt5ezVu9VxE26c93Zimpud10JAB+QLEA4HdbD5bq9+99L0madF13XdwlyXAiAP5CsQDgVyeq3MqZuVonqt26rFtr/frqbqYjAfAjigUAv3pswXptOVim1s3teumOTEUyVwGENIoFAL95L3+f5nxXKJtNevnOTLVJYK4CCHUUCwB+sf1wmR6ev06SNP6aCzSoW2vDiQA0BYoFAJ+rrHYr563Vqqhy65IurTTx2gtMRwLQRCgWAHzuyQ83aNOBUiXFx+jlO7OYqwDCCMUCgE99uLZIb32zRzab9NIdmUpJjDUdCUATolgA8JldxeV6cF7tXMWvr+qqK7q3MZwIQFOjWADwCVeNW+NmrVaZq0YXdWqpSdd1Nx0JgAEUCwA+MeWjjfp+n1Mt46L159FZiork1wsQjvjJB9BoC9ft1z++3i1JevH2TLVzNDOcCIApFAsAjbLnSIXun7dWkvS/V3TR1T2SDScCYBLFAkCDVdV4NH7WapVW1ii7Qwvd9/MLTUcCYBjFAkCDPbtok9bsLZGjWbReGZOtaOYqgLDHbwEADbJkw0G9vmKnJOmPo/qpfQvmKgBQLAA0wN5jFbrvnTWSpF8N6qzre6UYTgQgUFAsAHil2u3R+Fn5KjlRrX5pDj04pIfpSAACCMUCgFf+uHiz8vccV0JslKaOyVZMFL9GAPwHvxEAnLfPNh3UX5fvkCQ9P7Kv0lvFGU4EINBQLACcl/0lJ/Sbt2vnKu6+tKNuyGhnOBGAQESxAHBONW6PJszK17GKamW0T9TDN/U0HQlAgKJYADinlz7ZolW7jqm5PUpTR2fLHhVpOhKAAEWxAPCTlm85rFeXbpck5d7WR51axxtOBCCQUSwAnNVBZ6UmzSmQZUljLu6gYf1STUcCEOAoFgDOyO2xNHF2vo6UV6lH2wT9YWgv05EABAGKBYAzevnTrVq546jiYiI1bWy2YqOZqwBwbhQLAD/y5bZivfLZVknSlFv7qGub5oYTAQgWFAsApzlUWqmJs2vnKu4YkK5bstqbjgQgiFAsAJzi9liaNKdAxWUudU9prsdu7m06EoAgQ7EAcMqrn2/Tl9uOqFl0pKaNyVazGOYqAHiHYgFAkrRyxxG99MkWSdITw3vrgpQEw4kABCOKBQAdKXNp4ux8eSzptuz2GjUg3XQkAEGKYgGEOY/H0qS31+ig06WubeL15PAM05EABDGKBRDm/rJ8u5ZvOSx7VISmjc1WvD3KdCQAQYxiAYSxVbuO6oWPa+cqHr+5t3q0TTScCECwo1gAYepYeZUmzMqX22Pp5n6puuMi5ioANB7FAghDlmXpN++s0f6SSnVuHa8pt/WRzWYzHQtACKBYAGHob1/s1GebDikmKkJTx2SpOXMVAHyEYgGEmdV7junZRZskSb8f2ku9Ux2GEwEIJV4Vi9zcXF100UVKSEhQcnKybrnlFm3evNlf2QD4WElFtcbPzFeNx9JNfdrpros7mI4EIMR4VSyWLVumnJwcrVy5UkuWLFFNTY0GDx6s8vJyf+UD4COWZem+uWu07/gJdWgVp9wRzFUA8D2vTqwuWrTotPtvvPGGkpOTlZeXpyuuuMKnwQD41htf7tKSDQcVHWnTtDHZSoyNNh0JQAhq1MRWSUmJJKlVq1Zn/RqXyyWXy3XqvtPpbMxThrwP1xbpza93y+OxTEdBiFmz97gk6eEbe6pPGnMVAPyjwcXCsixNnjxZl112mTIyzr4CODc3V48//nhDnyasfLvzqCbMqr1eA+APN/Ruq/8e2Ml0DAAhzGZZVoPexnJycvTRRx9pxYoVSktLO+vXnemIRXp6ukpKSpSYyJa/OkfLq3Tjy1/ogLNSQzLaanhmqulICDHx9igN7NpakRHMVQDwntPplMPhOOf7d4OOWIwfP14LFizQ8uXLf7JUSJLdbpfdbm/I04QNj8fS5LcLdMBZqa5t4vXHUf24XgMAICh59e5lWZbGjx+v+fPna+nSpercubO/coWVGV/s0NLNXAQKABD8vHoHy8nJ0cyZM/X+++8rISFBBw4ckCQ5HA41a9bMLwFDXd7uo3p+ce0ukMe4CBQAIMh5tcdi+vTpKikp0VVXXaV27dqdus2ZM8df+ULa8YoqTZhVcOoiUHdyESgAQJDz+lQIfMOyLN33zlrtO36Ci0ABAEIG1wox5PUVO/XJxoNcBAoAEFIoFgYUFB7/z0WgburJRaAAACGDYtHESk5Ua9zM1ap2W7qxT1vddUlH05EAAPAZikUTsixL989do73Hai8C9cyIvsxVAABCCsWiCf3jq11avL72IlBTx2RxESgAQMihWDSRdXtLNOXftXMVD9/YU33TWpgNBACAH1AsmkBpZbXGzVqtKrdHP++dwkWgAAAhi2LhZ5Zl6cF312n3kQq1b9FMz43ox1wFACBkUSz87K1v9uijtfsVFVE7V+GIY64CABC6KBZ+tL6oRE98uEGS9MANPZTVoaXhRAAA+BfFwk/KXDUaNzNfVTUeXdsjWf9zOVeCBQCEPoqFH1iWpUfmr9PO4nKlOmL1x1HMVQAAwgPFwg/mrCrU+wVFioyw6ZUxWWoZH2M6EgAATYJi4WObD5Tq0QXrJUn3Db5Q/Tu2MpwIAICmQ7HwoYqqGuXMXC1XjUdXXdhG/3tFF9ORAABoUhQLH/r9e+u17VCZUhLtemFUP0VEMFcBAAgvFAsfmZu3V/NW71WETfrznVlKam43HQkAgCZHsfCBrQdL9fv3vpckTbquuy7ukmQ4EQAAZlAsGulElVs5M1frRLVbl3VrrV9f3c10JAAAjKFYNNJjC9Zry8EytUmw66U7MhXJXAUAIIxRLBrhvfx9mvNdoWw26eU7MtUmgbkKAEB4o1g00I7DZXp4/jpJ0oRrLtDAbq0NJwIAwDyKRQNUVruVMzNfFVVuXdKllSZce4HpSAAABASKRQM8+eEGbdzvVFJ8jF6+M4u5CgAATqJYeOnDtUV665s9stmkl+7IVEpirOlIAAAEDIqFF3YVl+vBebVzFb++qquu6N7GcCIAAAILxeI8uWrcGjdrtcpcNfpZp1aadF1305EAAAg4FIvzNOWjjfp+n1Mt46L18uhMRUXyVwcAwA/x7ngeFq7br398vVuS9OIdmWrnaGY4EQAAgYlicQ6FRyt0/7y1kqT/vbKLrr4w2XAiAAACF8XiJ1TVeDRu5mqVVtYou0ML3Tf4QtORAAAIaBSLn/Dsok1as7dEjmbRemVMtqKZqwAA4CfxTnkWSzYc1OsrdkqS/jiqn9q3YK4CAIBzoVicwd5jFbrvnTWSpP9zWWdd3yvFcCIAAIIDxeIHqt0ejZ+Vr5IT1eqX3kIP3NDDdCQAAIIGxeIH/rh4s/L3HFdCbJSmjs5STBR/RQAAnC/eNev5fNMh/XX5DknS8yP7Kb1VnOFEAAAEF4rFSftLTmjy2wWSpP8e2Ek3ZLQ1GwgAgCBEsZBU4/Zowqx8HauoVkb7RD10I3MVAAA0BMVC0kufbNGqXcfU3B6lqaOzZY+KNB0JAICgFPbFYvmWw3p16XZJ0jMj+qhT63jDiQAACF5hXSwOOis1aU6BLEsae3EHDe2bajoSAABBLWyLhdtjaeLsfB0pr1LPdon6/dBepiMBABD0wrZYvPzpVq3ccVTxMZGaNiZLsdHMVQAA0FhhWSy+2lasVz7bKkmaclsfdWnT3HAiAABCQ9gVi8OlLk08OVdxx4B0Dc9sbzoSAAAhI6yKhdtjadKcAh0udenClAQ9dnNv05EAAAgpYVUsXv18m1ZsK1az6EhNG5ulZjHMVQAA4EthUyxW7jiilz7ZIkl68pYMdUtOMJwIAIDQExbF4kiZSxNn58tjSSOy0zSyf5rpSAAAhKSQLxYej6VJb6/RQadL3ZKb68lbmKsAAMBfQr5Y/GX5di3fclix0RGaNiZbcTFRpiMBABCyQrpYfLfrqF74uHau4vGbe+vCtsxVAADgTyFbLI6VV2n8rHy5PZaGZ6bq9gHppiMBABDyQrJYWJal+95Zo/0llerSOl5P39pHNpvNdCwAAEJeSBaLv32xU59uOqSYqAhNHZOt5nbmKgAAaApeF4vly5dr2LBhSk1Nlc1m03vvveeHWA23es8xPbtokyTpD0N7qVdqouFEAACED6+LRXl5ufr166epU6f6I0+jlFRUa/zMfNV4LN3Ut53GXtzBdCQAAMKK1+cIhgwZoiFDhvgjS6NYlqX75q7RvuMn1DEpTs/cxlwFAABNze/DBy6XSy6X69R9p9Ppl+d548tdWrLhoGIia/dVJMRG++V5AADA2fl9eDM3N1cOh+PULT3d9x/7PFBSqWcW1s5VPHxjD2W0d/j8OQAAwLn5vVg89NBDKikpOXUrLCz0+XO0dcRq2ths3TEgXXcP7OTzPx8AAJwfv58Ksdvtstvt/n4aXd8rRdf3SvH78wAAgLMLyT0WAADADK+PWJSVlWnbtm2n7u/cuVMFBQVq1aqVOnTg450AAIQzr4vFd999p6uvvvrU/cmTJ0uS7r77bv3973/3WTAAABB8vC4WV111lSzL8kcWAAAQ5JixAAAAPkOxAAAAPkOxAAAAPkOxAAAAPkOxAAAAPkOxAAAAPkOxAAAAPkOxAAAAPkOxAAAAPuP3q5v+UN3WTqfT2dRPDQAAGqjufftc27ebvFiUlpZKktLT05v6qQEAQCOVlpbK4XCc9Z/brCa+8IfH41FRUZESEhJks9l89uc6nU6lp6ersLBQiYmJPvtz0TC8HoGH1ySw8HoEFl6Pc7MsS6WlpUpNTVVExNknKZr8iEVERITS0tL89ucnJibyH0UA4fUIPLwmgYXXI7Dwevy0nzpSUYfhTQAA4DMUCwAA4DMhUyzsdrseffRR2e1201EgXo9AxGsSWHg9Aguvh+80+fAmAAAIXSFzxAIAAJhHsQAAAD5DsQAAAD5DsQAAAD4TMsXi1VdfVefOnRUbG6v+/fvriy++MB0pLOXm5uqiiy5SQkKCkpOTdcstt2jz5s2mY+Gk3Nxc2Ww23XvvvaajhK19+/bprrvuUlJSkuLi4pSZmam8vDzTscJWTU2Nfve736lz585q1qyZunTpoieeeEIej8d0tKAVEsVizpw5uvfee/XII48oPz9fl19+uYYMGaI9e/aYjhZ2li1bppycHK1cuVJLlixRTU2NBg8erPLyctPRwt6qVas0Y8YM9e3b13SUsHXs2DENGjRI0dHRWrhwoTZs2KAXXnhBLVq0MB0tbD377LP6y1/+oqlTp2rjxo167rnn9Pzzz+uVV14xHS1ohcTHTS+++GJlZ2dr+vTppx7r2bOnbrnlFuXm5hpMhsOHDys5OVnLli3TFVdcYTpO2CorK1N2drZeffVVPfXUU8rMzNSf/vQn07HCzoMPPqgvv/ySI6oBZOjQoUpJSdHrr79+6rERI0YoLi5O//znPw0mC15Bf8SiqqpKeXl5Gjx48GmPDx48WF999ZWhVKhTUlIiSWrVqpXhJOEtJydHN910k6677jrTUcLaggULNGDAAI0aNUrJycnKysrSa6+9ZjpWWLvsssv06aefasuWLZKkNWvWaMWKFbrxxhsNJwteTX4RMl8rLi6W2+1WSkrKaY+npKTowIEDhlJBqr0S3uTJk3XZZZcpIyPDdJywNXv2bK1evVqrVq0yHSXs7dixQ9OnT9fkyZP18MMP69tvv9WECRNkt9v1X//1X6bjhaUHHnhAJSUl6tGjhyIjI+V2u/X0009r9OjRpqMFraAvFnV+eAl2y7J8ell2eG/cuHFau3atVqxYYTpK2CosLNTEiRP18ccfKzY21nScsOfxeDRgwABNmTJFkpSVlaX169dr+vTpFAtD5syZo3/961+aOXOmevfurYKCAt17771KTU3V3XffbTpeUAr6YtG6dWtFRkb+6OjEoUOHfnQUA01n/PjxWrBggZYvX660tDTTccJWXl6eDh06pP79+596zO12a/ny5Zo6dapcLpciIyMNJgwv7dq1U69evU57rGfPnpo3b56hRPjtb3+rBx98UHfeeackqU+fPtq9e7dyc3MpFg0U9DMWMTEx6t+/v5YsWXLa40uWLNHAgQMNpQpflmVp3Lhxevfdd/XZZ5+pc+fOpiOFtWuvvVbr1q1TQUHBqduAAQM0duxYFRQUUCqa2KBBg3708estW7aoY8eOhhKhoqJCERGnvxVGRkbycdNGCPojFpI0efJk/eIXv9CAAQN06aWXasaMGdqzZ4/uuece09HCTk5OjmbOnKn3339fCQkJp44kORwONWvWzHC68JOQkPCj+Zb4+HglJSUx92LApEmTNHDgQE2ZMkW33367vv32W82YMUMzZswwHS1sDRs2TE8//bQ6dOig3r17Kz8/Xy+++KJ+9atfmY4WvKwQMW3aNKtjx45WTEyMlZ2dbS1btsx0pLAk6Yy3N954w3Q0nHTllVdaEydONB0jbH3wwQdWRkaGZbfbrR49elgzZswwHSmsOZ1Oa+LEiVaHDh2s2NhYq0uXLtYjjzxiuVwu09GCVkjssQAAAIEh6GcsAABA4KBYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn6FYAAAAn/n/VuylFHk6m7IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "df = df.sort_values(by=\"Value\")\n",
+    "plt.plot(df[\"Value\"].values)\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,21 +310,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 111,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "f_df = df.groupby(\"Value\").agg(\"count\").reset_index()\n",
+    "plt.plot(f_df[\"Value\"],f_df[\"Roll\"])\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 94,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe difference between thoose two plot is the following:\\n\\nthe first plot shows the values of the rolls, sorted by value\\nthe second plot shows the count of each value, unsorted \\n\\nthe relation is only the count \\n'"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The difference between thoose two plot is the following:\n",
+    "\n",
+    "the first plot shows the values of the rolls, sorted by value\n",
+    "the second plot shows the count of each value, unsorted \n",
+    "\n",
+    "the relation is only the count \n",
     "\"\"\""
    ]
   },
@@ -91,11 +375,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 142,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.2"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def summary_stats(df, column):\n",
+    "    x = 0\n",
+    "    for i in df[column]:\n",
+    "        x += i\n",
+    "    mean = x/len(df)\n",
+    "    return mean\n",
+    "\n",
+    "summary_stats(roll_dice(), \"Value\")"
    ]
   },
   {
@@ -107,11 +409,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 116,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Roll</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Value  Roll\n",
+       "0      1     1\n",
+       "1      2     4\n",
+       "2      3     1\n",
+       "3      4     3\n",
+       "4      6     1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "sorted_df = df.groupby(\"Value\").agg(\"count\").reset_index()\n",
+    "display(sorted_df)\n",
+    "mean = sum(sorted_df[\"Roll\"]) / len(sorted_df[\"Roll\"])\n",
+    "print(round(mean,2))"
    ]
   },
   {
@@ -124,11 +506,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 117,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "def get_median():\n",
+    "    x = df[\"Value\"].reset_index(drop=True)\n",
+    "    n = len(\"Value\")\n",
+    "    if n%2 == 0:\n",
+    "        return (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        return x.iloc[int((n-1)/2)]\n",
+    "    \n",
+    "median = get_median()\n",
+    "print(median)"
    ]
   },
   {
@@ -140,11 +539,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 120,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 2\n",
+      "Q2: 2.5\n",
+      "Q3: 4\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "def quartiles():\n",
+    "    x = df[\"Value\"].reset_index(drop=True)\n",
+    "    x = x.sort_values()\n",
+    "    n = len(x)    \n",
+    "\n",
+    "    if n % 4 == 0:\n",
+    "        q1 = (x.iloc[int(n / 4)] + x.iloc[int(n / 4) - 1]) / 2\n",
+    "        q3 = (x.iloc[int(n * 3 / 4)] + x.iloc[int(n * 3 / 4) - 1]) / 2\n",
+    "    else:\n",
+    "        q1 = x.iloc[int(n / 4)]\n",
+    "        q3 = x.iloc[int(n * 3 / 4)]\n",
+    "        \n",
+    "\n",
+    "    if n % 2 == 0:\n",
+    "        q2 = (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        q2 = x.iloc[int((n - 1) / 2)]\n",
+    "\n",
+    "    return q1, q2, q3\n",
+    "\n",
+    "    \n",
+    "result_q1, result_q2, result_q3 = quartiles()\n",
+    "print(\"Q1:\", result_q1)\n",
+    "print(\"Q2:\", result_q2)\n",
+    "print(\"Q3:\", result_q3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n",
+      "2.5\n",
+      "4.0\n",
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Value\"].quantile(0.25))\n",
+    "print(df[\"Value\"].quantile(0.5))\n",
+    "print(df[\"Value\"].quantile(0.75))\n",
+    "print(df[\"Value\"].mean())"
    ]
   },
   {
@@ -158,21 +614,306 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "1            1     1      2\n",
+       "2            2     2      6\n",
+       "3            3     3      1\n",
+       "4            4     4      6\n",
+       "..         ...   ...    ...\n",
+       "95          95    95      4\n",
+       "96          96    96      6\n",
+       "97          97    97      1\n",
+       "98          98    98      3\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "new_df = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "\n",
+    "new_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "new_df.drop(\"Unnamed: 0\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    roll  value\n",
+       "0      0      1\n",
+       "1      1      2\n",
+       "2      2      6\n",
+       "3      3      1\n",
+       "4      4      6\n",
+       "..   ...    ...\n",
+       "95    95      4\n",
+       "96    96      6\n",
+       "97    97      1\n",
+       "98    98      3\n",
+       "99    99      6\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_df = new_df.sort_values(by=\"value\")\n",
+    "x = new_df[\"value\"]\n",
+    "y = new_df[\"roll\"]\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nEven though the values should be equally distributed, it seems like 6 appears significantly more often than 5 or 3 \\n'"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Even though the values should be equally distributed, it seems like 6 appears significantly more often than 5 or 3 \n",
     "\"\"\""
    ]
   },
@@ -185,11 +926,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 143,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "summary_stats(new_df, \"value\")"
    ]
   },
   {
@@ -201,11 +953,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 145,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   value  Count\n",
+       "0      1     12\n",
+       "1      2     17\n",
+       "2      3     14\n",
+       "3      4     22\n",
+       "4      5     12\n",
+       "5      6     23"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "sorted_df = new_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_df.rename(columns={\"roll\":\"Count\"},inplace=True)\n",
+    "sorted_df"
    ]
   },
   {
@@ -217,11 +1046,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 149,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.hist(new_df['value'], bins='auto', edgecolor='black')  \n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
    ]
   },
   {
@@ -244,11 +1087,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 151,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "dice_df = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "dice_df.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
+    "\n",
+    "sorted_dice = dice_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_dice.rename(columns={\"roll\":\"num_of_rolls\"},inplace=True)\n",
+    "x = sorted_dice[\"value\"]\n",
+    "y = sorted_dice[\"num_of_rolls\"]\n",
+    "plt.plot(x,y)\n",
+    "plt.show()"
    ]
   },
   {
@@ -258,7 +1120,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Due to the higher amount of Rolls it seems like the graph is more equaly distributed\n",
     "\"\"\""
    ]
   },
@@ -272,13 +1134,45 @@
     "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "plt.hist(age_pop['observation'], bins='auto', edgecolor='black')  \n",
+    "\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "\"\"\"\n",
+    "Guesses:\n",
+    "mean = 38\n",
+    "std 20\n",
+    "\n",
+    "\"\"\""
    ]
   },
   {
@@ -290,21 +1184,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 154,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 36.56\n",
+      "Standard Deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "\n",
+    "print(f\"\"\"Mean : {age_pop[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop[\"observation\"].std()}\"\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 155,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nmean was good guess, standart deviation not really\\n'"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "mean was good guess, standart deviation not really\n",
     "\"\"\""
    ]
   },
@@ -317,11 +1233,200 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 157,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation  count\n",
+       "0          28.0    139\n",
+       "1          27.0    125\n",
+       "2          26.0    120\n",
+       "3          29.0    115\n",
+       "4          25.0     98\n",
+       "5          30.0     90\n",
+       "6          24.0     78\n",
+       "7          31.0     61\n",
+       "8          23.0     41\n",
+       "9          22.0     35\n",
+       "10         32.0     31\n",
+       "11         33.0     22\n",
+       "12         21.0     17\n",
+       "13         20.0     13\n",
+       "14         34.0      7\n",
+       "15         35.0      3\n",
+       "16         19.0      3\n",
+       "17         36.0      2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "freq = age2.value_counts().reset_index()\n",
+    "freq.columns = [\"observation\", \"count\"]\n",
+    "y = freq[\"observation\"]\n",
+    "x = freq[\"count\"]\n",
+    "display(freq)\n",
+    "plt.plot(x,y,c=\"skyblue\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.hist(age_pop2['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
    ]
   },
   {
@@ -338,7 +1443,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "shape changed a bit, more clear and also more equally distributed\n",
     "\"\"\""
    ]
   },
@@ -351,11 +1456,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 160,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 36.56\n",
+      "Standard Deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(f\"\"\"Mean : {age_pop[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 27.155\n",
+      "Standard Deviation: 2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop2[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop2[\"observation\"].std()}\"\"\")"
    ]
   },
   {
@@ -365,7 +1499,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "mean decreased, huge drop in the standart deviation ==> more equally distribution as i asumed\n",
     "\"\"\""
    ]
   },
@@ -381,11 +1515,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 162,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "plt.hist(age_pop3['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
    ]
   },
   {
@@ -397,11 +1546,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 163,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 41.989\n",
+      "Standard Deviation: 16.144705959865934\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(f\"\"\"Mean : {age_pop3[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop3[\"observation\"].std()}\"\"\")"
    ]
   },
   {
@@ -411,7 +1570,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "standart deviation increased again a lot, mean also increased a lot\n",
     "\"\"\""
    ]
   },
@@ -424,85 +1583,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 165,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0\n",
+      "40.0\n",
+      "53.0\n",
+      "77.0\n",
+      "41.989\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(age_pop3[\"observation\"].quantile(0.25))\n",
+    "print(age_pop3[\"observation\"].quantile(0.5))\n",
+    "print(age_pop3[\"observation\"].quantile(0.75))\n",
+    "print(age_pop3[\"observation\"].quantile(1))\n",
+    "print(age_pop3[\"observation\"].mean())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 166,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe difference between mean and medain is around 1.9 years. 50 % of the people in this neighborhood are between 30 and 53 \\nyears old.\\n'"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The difference between mean and medain is around 1.9 years. 50 % of the people in this neighborhood are between 30 and 53 \n",
+    "years old.\n",
     "\"\"\""
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus challenge\n",
-    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1653,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,